


'''simple docstring'''


import unittest

import torch

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


lowerCamelCase__    	=     False






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     Optional[int]=32 )   ->str:



					'''simple docstring'''







					set_seed(0 )
					_UpperCAmelCase		:       int								=      UNetaDModel(sample_size=lowerCamelCase__	,  in_channels=3	,  out_channels=3 )
					_UpperCAmelCase		:       Union[str, Any]								=      torch.optim.SGD(model.parameters()	,  lr=0.0_0_0_1 )
					return model, optimizer






			@slow
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
					_UpperCAmelCase		:       str								=      DDPMScheduler(
					    num_train_timesteps=10_00	,  beta_start=0.0_0_0_1	,  beta_end=0.0_2	,  beta_schedule="linear"	,  clip_sample=lowerCamelCase__	,  )
					_UpperCAmelCase		:       List[str]								=      DDIMScheduler(
					    num_train_timesteps=10_00	,  beta_start=0.0_0_0_1	,  beta_end=0.0_2	,  beta_schedule="linear"	,  clip_sample=lowerCamelCase__	,  )

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

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

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

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

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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__    	=     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'InstructBlipQFormerModel',
						    'InstructBlipPreTrainedModel',
						    'InstructBlipForConditionalGeneration',
						    'InstructBlipVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

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



