

'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
     import transformers




def 	UpperCamelCase   (       lowercase_						:    int	)		->	List[str]:




  '''simple docstring'''



  random.seed(lowercase_	)
  np.random.seed(lowercase_	)
  torch.manual_seed(lowercase_	)
  torch.cuda.manual_seed_all(lowercase_	)
  # ^^ safe to call this function even if cuda is not available




class 						__magic_name__   :







 def __init__(       self  , snake_case_  , snake_case_ = 0.99_99  , snake_case_ = 0.0  , snake_case_ = 0  , snake_case_ = False  , snake_case_ = 1.0  , snake_case_ = 2 / 3  , snake_case_ = None  , snake_case_ = None  , **snake_case_  , ):

   if isinstance(snake_case_  , torch.nn.Module   ):
     lowercase									=(
         '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
         '''Please pass the parameters of the module instead.'''
     )
     deprecate(
         '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''  , '''1.0.0'''  , snake_case_  , standard_warn=snake_case_  , )
     lowercase									=parameters.parameters()

     # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
     lowercase									=True

   if kwargs.get('''max_value'''  , snake_case_   ) is not None:
     lowercase									='''The `max_value` argument is deprecated. Please use `decay` instead.'''
     deprecate('''max_value'''  , '''1.0.0'''  , snake_case_  , standard_warn=snake_case_   )
     lowercase									=kwargs['''max_value''']

   if kwargs.get('''min_value'''  , snake_case_   ) is not None:
     lowercase									='''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
     deprecate('''min_value'''  , '''1.0.0'''  , snake_case_  , standard_warn=snake_case_   )
     lowercase									=kwargs['''min_value''']

   lowercase									=list(snake_case_   )
   lowercase									=[p.clone().detach() for p in parameters]

   if kwargs.get('''device'''  , snake_case_   ) is not None:
     lowercase									='''The `device` argument is deprecated. Please use `to` instead.'''
     deprecate('''device'''  , '''1.0.0'''  , snake_case_  , standard_warn=snake_case_   )
     self.to(device=kwargs['''device''']   )

   lowercase									=None

   lowercase									=decay
   lowercase									=min_decay
   lowercase									=update_after_step
   lowercase									=use_ema_warmup
   lowercase									=inv_gamma
   lowercase									=power
   lowercase									=0
   lowercase									=None  # set in `step()`

   lowercase									=model_cls
   lowercase									=model_config







 @classmethod
 def 						_A(       cls  , snake_case_  , snake_case_   ):
   lowercase							, lowercase									=model_cls.load_config(snake_case_  , return_unused_kwargs=snake_case_   )
   lowercase									=model_cls.from_pretrained(snake_case_   )

   lowercase									=cls(model.parameters()  , model_cls=snake_case_  , model_config=model.config   )

   ema_model.load_state_dict(snake_case_   )
   return ema_model







 def 						_A(       self  , snake_case_   ):
   if self.model_cls is None:
     raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''   )

   if self.model_config is None:
     raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''   )

   lowercase									=self.model_cls.from_config(self.model_config   )
   lowercase									=self.state_dict()
   state_dict.pop('''shadow_params'''  , snake_case_   )

   model.register_to_config(**snake_case_   )
   self.copy_to(model.parameters()   )
   model.save_pretrained(snake_case_   )







 def 						_A(       self  , snake_case_   ):
   lowercase									=max(0  , optimization_step - self.update_after_step - 1   )

   if step <= 0:
     return 0.0

   if self.use_ema_warmup:
     lowercase									=1 - (1 + step / self.inv_gamma) ** -self.power
   else:
     lowercase									=(1 + step) / (10 + step)

   lowercase									=min(snake_case_  , self.decay   )
   # make sure decay is not smaller than min_decay
   lowercase									=max(snake_case_  , self.min_decay   )
   return cur_decay_value







 @torch.no_grad()
 def 						_A(       self  , snake_case_   ):
   if isinstance(snake_case_  , torch.nn.Module   ):
     lowercase									=(
         '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
         '''Please pass the parameters of the module instead.'''
     )
     deprecate(
         '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''  , '''1.0.0'''  , snake_case_  , standard_warn=snake_case_  , )
     lowercase									=parameters.parameters()

   lowercase									=list(snake_case_   )

   self.optimization_step += 1

   # Compute the decay factor for the exponential moving average.
   lowercase									=self.get_decay(self.optimization_step   )
   lowercase									=decay
   lowercase									=1 - decay

   lowercase									=contextlib.nullcontext
   if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
     import deepspeed

   for s_param, param in zip(self.shadow_params  , snake_case_   ):
     if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
       lowercase									=deepspeed.zero.GatheredParameters(snake_case_  , modifier_rank=snake_case_   )

     with context_manager():
       if param.requires_grad:
         s_param.sub_(one_minus_decay * (s_param - param)   )
       else:
         s_param.copy_(snake_case_   )







 def 						_A(       self  , snake_case_   ):
   lowercase									=list(snake_case_   )
   for s_param, param in zip(self.shadow_params  , snake_case_   ):
     param.data.copy_(s_param.to(param.device   ).data   )







 def 						_A(       self  , snake_case_=None  , snake_case_=None   ):
   lowercase									=[
       p.to(device=snake_case_  , dtype=snake_case_   ) if p.is_floating_point() else p.to(device=snake_case_   )
       for p in self.shadow_params
   ]







 def 						_A(       self   ):
   return {
       "decay": self.decay,
       "min_decay": self.min_decay,
       "optimization_step": self.optimization_step,
       "update_after_step": self.update_after_step,
       "use_ema_warmup": self.use_ema_warmup,
       "inv_gamma": self.inv_gamma,
       "power": self.power,
       "shadow_params": self.shadow_params,
   }







 def 						_A(       self  , snake_case_   ):
   lowercase									=[param.detach().cpu().clone() for param in parameters]







 def 						_A(       self  , snake_case_   ):
   if self.temp_stored_params is None:
     raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''   )
   for c_param, param in zip(self.temp_stored_params  , snake_case_   ):
     param.data.copy_(c_param.data   )

   # Better memory-wise.
   lowercase									=None







 def 						_A(       self  , snake_case_   ):
   lowercase									=copy.deepcopy(snake_case_   )

   lowercase									=state_dict.get('''decay'''  , self.decay   )
   if self.decay < 0.0 or self.decay > 1.0:
     raise ValueError('''Decay must be between 0 and 1'''   )

   lowercase									=state_dict.get('''min_decay'''  , self.min_decay   )
   if not isinstance(self.min_decay  , snake_case_   ):
     raise ValueError('''Invalid min_decay'''   )

   lowercase									=state_dict.get('''optimization_step'''  , self.optimization_step   )
   if not isinstance(self.optimization_step  , snake_case_   ):
     raise ValueError('''Invalid optimization_step'''   )

   lowercase									=state_dict.get('''update_after_step'''  , self.update_after_step   )
   if not isinstance(self.update_after_step  , snake_case_   ):
     raise ValueError('''Invalid update_after_step'''   )

   lowercase									=state_dict.get('''use_ema_warmup'''  , self.use_ema_warmup   )
   if not isinstance(self.use_ema_warmup  , snake_case_   ):
     raise ValueError('''Invalid use_ema_warmup'''   )

   lowercase									=state_dict.get('''inv_gamma'''  , self.inv_gamma   )
   if not isinstance(self.inv_gamma  , (float, int)   ):
     raise ValueError('''Invalid inv_gamma'''   )

   lowercase									=state_dict.get('''power'''  , self.power   )
   if not isinstance(self.power  , (float, int)   ):
     raise ValueError('''Invalid power'''   )

   lowercase									=state_dict.get('''shadow_params'''  , snake_case_   )
   if shadow_params is not None:
     lowercase									=shadow_params
     if not isinstance(self.shadow_params  , snake_case_   ):
       raise ValueError('''shadow_params must be a list'''   )
     if not all(isinstance(snake_case_  , torch.Tensor   ) for p in self.shadow_params   ):
       raise ValueError('''shadow_params must all be Tensors'''   )




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				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			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				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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





