



import unittest

import torch

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


lowercase_	       =			False






class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    List[Any]=32)->							List[str]:
						set_seed(0)
						__lowerCAmelCase	   =UNetaDModel(sample_size=snake_case_				,	in_channels=3				,	out_channels=3)
						__lowerCAmelCase	   =torch.optim.SGD(model.parameters()				,	lr=0.0_0_0_1)
						return model, optimizer



	@slow
	def 		UpperCamelCase							(						self							:    List[str])->							Optional[Any]:
						__lowerCAmelCase	   ="""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
						__lowerCAmelCase	   =DDPMScheduler(
						    num_train_timesteps=10_00				,	beta_start=0.0_0_0_1				,	beta_end=0.0_2				,	beta_schedule="""linear"""				,	clip_sample=snake_case_				,	)
						__lowerCAmelCase	   =DDIMScheduler(
						    num_train_timesteps=10_00				,	beta_start=0.0_0_0_1				,	beta_end=0.0_2				,	beta_schedule="""linear"""				,	clip_sample=snake_case_				,	)

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

						# shared batches for DDPM and DDIM
						set_seed(0)
						__lowerCAmelCase	   =[torch.randn((4, 3, 32, 32)).clip(-1				,	1).to(snake_case_) for _ in range(4)]
						__lowerCAmelCase	   =[torch.randn((4, 3, 32, 32)).to(snake_case_) for _ in range(4)]
						__lowerCAmelCase	   =[torch.randint(0				,	10_00				,	(4,)).long().to(snake_case_) for _ in range(4)]

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

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

						self.assertTrue(torch.allclose(snake_case_				,	snake_case_				,	atol=1e-5))
						self.assertTrue(torch.allclose(snake_case_				,	snake_case_				,	atol=1e-5))









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_	       =			{
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_	       =			[
				    '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''Blip2Model''',
				    '''Blip2QFormerModel''',
				    '''Blip2PreTrainedModel''',
				    '''Blip2ForConditionalGeneration''',
				    '''Blip2VisionModel''',
				]

if TYPE_CHECKING:
				from .configuration_blip_a import (
				    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BlipaConfig,
				    BlipaQFormerConfig,
				    BlipaVisionConfig,
				)
				from .processing_blip_a import BlipaProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_blip_a import (
								    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BlipaForConditionalGeneration,
								    BlipaModel,
								    BlipaPreTrainedModel,
								    BlipaQFormerModel,
								    BlipaVisionModel,
								)

else:
				import sys

				lowercase_	       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




