
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


UpperCAmelCase       =					False







class 			A_  (     unittest.TestCase  ):




	'''simple docstring'''


	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case=32			):
		set_seed(0			)
		lowercase	    =       UNetaDModel(sample_size=__UpperCAmelCase						,       in_channels=3						,       out_channels=3			)
		lowercase	    =       torch.optim.SGD(model.parameters()						,       lr=0.0_001			)
		return model, optimizer





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
		lowercase	    =       DDPMScheduler(
		    num_train_timesteps=1000						,       beta_start=0.0_001						,       beta_end=0.02						,       beta_schedule='linear'						,       clip_sample=__UpperCAmelCase						,       )
		lowercase	    =       DDIMScheduler(
		    num_train_timesteps=1000						,       beta_start=0.0_001						,       beta_end=0.02						,       beta_schedule='linear'						,       clip_sample=__UpperCAmelCase						,       )

		assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

		# shared batches for DDPM and DDIM
		set_seed(0			)
		lowercase	    =       [torch.randn((4, 3, 32, 32)			).clip(-1						,       1			).to(__UpperCAmelCase			) for _ in range(4			)]
		lowercase	    =       [torch.randn((4, 3, 32, 32)			).to(__UpperCAmelCase			) for _ in range(4			)]
		lowercase	    =       [torch.randint(0						,       1000						,       (4,)			).long().to(__UpperCAmelCase			) for _ in range(4			)]

		# train with a DDPM scheduler
		lowercase    ,   lowercase	    =       self.get_model_optimizer(resolution=32			)
		model.train().to(__UpperCAmelCase			)
		for i in range(4			):
			optimizer.zero_grad()
			lowercase	    =       ddpm_scheduler.add_noise(clean_images[i]						,       noise[i]						,       timesteps[i]			)
			lowercase	    =       model(__UpperCAmelCase						,       timesteps[i]			).sample
			lowercase	    =       torch.nn.functional.mse_loss(__UpperCAmelCase						,       noise[i]			)
			loss.backward()
			optimizer.step()
		del model, optimizer

		# recreate the model and optimizer, and retry with DDIM
		lowercase    ,   lowercase	    =       self.get_model_optimizer(resolution=32			)
		model.train().to(__UpperCAmelCase			)
		for i in range(4			):
			optimizer.zero_grad()
			lowercase	    =       ddim_scheduler.add_noise(clean_images[i]						,       noise[i]						,       timesteps[i]			)
			lowercase	    =       model(__UpperCAmelCase						,       timesteps[i]			).sample
			lowercase	    =       torch.nn.functional.mse_loss(__UpperCAmelCase						,       noise[i]			)
			loss.backward()
			optimizer.step()
		del model, optimizer

		self.assertTrue(torch.allclose(__UpperCAmelCase						,       __UpperCAmelCase						,       atol=1E-5			)			)
		self.assertTrue(torch.allclose(__UpperCAmelCase						,       __UpperCAmelCase						,       atol=1E-5			)			)






def 		a__      (		_UpperCamelCase      :			list[int]				):
   if not numbers:
      return 0

   if not isinstance(_UpperCamelCase			,(list, tuple)				) or not all(
       isinstance(_UpperCamelCase			,_UpperCamelCase				) for number in numbers				):
      raise ValueError('''numbers must be an iterable of integers'''				)

   __lowerCamelCase    				=     __lowerCamelCase    				=     __lowerCamelCase    				=     numbers[0]

   for i in range(1			,len(_UpperCamelCase				)				):
      # update the maximum and minimum subarray products
      __lowerCamelCase    				=     numbers[i]
      if number < 0:
         __lowerCamelCase				,__lowerCamelCase    				=     min_till_now, max_till_now
      __lowerCamelCase    				=     max(_UpperCamelCase			,max_till_now * number				)
      __lowerCamelCase    				=     min(_UpperCamelCase			,min_till_now * number				)

      # update the maximum product found till now
      __lowerCamelCase    				=     max(_UpperCamelCase			,_UpperCamelCase				)

   return max_prod






