"""simple docstring"""







import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()






class 							__snake_case(					unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			"hf-internal-testing/tiny-stable-diffusion-pipe"
    _UpperCAmelCase       =			1
    _UpperCAmelCase       =			7_7
    _UpperCAmelCase       =			3_2
    _UpperCAmelCase       =			4
    _UpperCAmelCase       =			_UpperCAmelCase       =			6_4
    _UpperCAmelCase       =			torch.manual_seed(0	)
    def 						_a							(  self					,       __lowerCamelCase=0.99_99	):




         '''simple docstring'''



         __A					:					str								=		UNetaDConditionModel.from_pretrained(self.model_id					,       subfolder='unet'	)
         __A					:					Union[str, Any]								=		unet.to(__lowerCamelCase	)
         __A					:					Optional[Any]								=		EMAModel(unet.parameters()					,       decay=__lowerCamelCase					,       model_cls=__lowerCamelCase					,       model_config=unet.config	)
         return unet, ema_unet
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					List[Any]								=		torch.randn(
             self.batch_size					,       self.num_in_channels					,       self.latent_height					,       self.latent_width					,       generator=self.generator	).to(__lowerCamelCase	)
         __A					:					Union[str, Any]								=		torch.randint(0					,       1000					,       size=(self.batch_size,)					,       generator=self.generator	).to(__lowerCamelCase	)
         __A					:					Any								=		torch.randn(
             self.batch_size					,       self.prompt_length					,       self.text_encoder_hidden_dim					,       generator=self.generator	).to(__lowerCamelCase	)
         return noisy_latents, timesteps, encoder_hidden_states
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[Any]								=		{}
         for k, param in unet.state_dict().items():
              __A					:					List[str]								=		torch.randn_like(__lowerCamelCase	) + (param * torch.randn_like(__lowerCamelCase	))
              updated_state_dict.update({k: updated_param}	)
         unet.load_state_dict(__lowerCamelCase	)
         return unet
    def 						_a							(  self	):




         '''simple docstring'''



         __A						,		__A					:					Union[str, Any]								=		self.get_models()
         # Take the first (hypothetical) EMA step.
         ema_unet.step(unet.parameters()	)
         assert ema_unet.optimization_step == 1

         # Take two more.
         for _ in range(2	):
              ema_unet.step(unet.parameters()	)
         assert ema_unet.optimization_step == 3
    def 						_a							(  self	):




         '''simple docstring'''



         __A						,		__A					:					Optional[Any]								=		self.get_models()
         # Since the `unet` is not being updated (i.e., backprop'd)
         # there won't be any difference between the `params` of `unet`
         # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
         ema_unet.step(unet.parameters()	)
         __A					:					Union[str, Any]								=		list(unet.parameters()	)
         for s_param, param in zip(ema_unet.shadow_params					,       __lowerCamelCase	):
              assert torch.allclose(__lowerCamelCase					,       __lowerCamelCase	)

         # The above holds true even if we call `ema.step()` multiple times since
         # `unet` params are still not being updated.
         for _ in range(4	):
              ema_unet.step(unet.parameters()	)
         for s_param, param in zip(ema_unet.shadow_params					,       __lowerCamelCase	):
              assert torch.allclose(__lowerCamelCase					,       __lowerCamelCase	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A						,		__A					:					Optional[Any]								=		self.get_models()
         # Here we simulate the parameter updates for `unet`. Since there might
         # be some parameters which are initialized to zero we take extra care to
         # initialize their values to something non-zero before the multiplication.
         __A					:					Any								=		self.simulate_backprop(__lowerCamelCase	)

         # Take the EMA step.
         ema_unet.step(unet_pseudo_updated_step_one.parameters()	)

         # Now the EMA'd parameters won't be equal to the original model parameters.
         __A					:					Tuple								=		list(unet_pseudo_updated_step_one.parameters()	)
         for s_param, param in zip(ema_unet.shadow_params					,       __lowerCamelCase	):
              assert ~torch.allclose(__lowerCamelCase					,       __lowerCamelCase	)

         # Ensure this is the case when we take multiple EMA steps.
         for _ in range(4	):
              ema_unet.step(unet.parameters()	)
         for s_param, param in zip(ema_unet.shadow_params					,       __lowerCamelCase	):
              assert ~torch.allclose(__lowerCamelCase					,       __lowerCamelCase	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A						,		__A					:					Union[str, Any]								=		self.get_models()

         # First backprop + EMA
         __A					:					Dict								=		self.simulate_backprop(__lowerCamelCase	)
         ema_unet.step(unet_step_one.parameters()	)
         __A					:					List[Any]								=		ema_unet.shadow_params

         # Second backprop + EMA
         __A					:					List[Any]								=		self.simulate_backprop(__lowerCamelCase	)
         ema_unet.step(unet_step_two.parameters()	)
         __A					:					Dict								=		ema_unet.shadow_params

         for step_one, step_two in zip(__lowerCamelCase					,       __lowerCamelCase	):
              assert ~torch.allclose(__lowerCamelCase					,       __lowerCamelCase	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A						,		__A					:					Optional[int]								=		self.get_models(decay=0.0	)
         __A					:					int								=		self.simulate_backprop(__lowerCamelCase	)
         ema_unet.step(unet_step_one.parameters()	)
         __A					:					List[Any]								=		ema_unet.shadow_params

         __A					:					Optional[Any]								=		self.simulate_backprop(__lowerCamelCase	)
         ema_unet.step(unet_step_two.parameters()	)
         __A					:					Tuple								=		ema_unet.shadow_params

         for step_one, step_two in zip(__lowerCamelCase					,       __lowerCamelCase	):
              assert torch.allclose(__lowerCamelCase					,       __lowerCamelCase	)

    @skip_mps
    def 						_a							(  self	):




         '''simple docstring'''



         __A						,		__A					:					int								=		self.get_models()
         __A						,		__A						,		__A					:					Union[str, Any]								=		self.get_dummy_inputs()

         with tempfile.TemporaryDirectory() as tmpdir:
              ema_unet.save_pretrained(__lowerCamelCase	)
              __A					:					List[Any]								=		UNetaDConditionModel.from_pretrained(__lowerCamelCase					,       model_cls=__lowerCamelCase	)
              __A					:					int								=		loaded_unet.to(unet.device	)

         # Since no EMA step has been performed the outputs should match.
         __A					:					Tuple								=		unet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	).sample
         __A					:					List[str]								=		loaded_unet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	).sample

         assert torch.allclose(__lowerCamelCase					,       __lowerCamelCase					,       atol=1e-4	)




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      : Dict       ={'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase      : List[str]       =[
           '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''SEWDForCTC''',
           '''SEWDForSequenceClassification''',
           '''SEWDModel''',
           '''SEWDPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_sew_d import (
                  SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SEWDForCTC,
                  SEWDForSequenceClassification,
                  SEWDModel,
                  SEWDPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase      : str       =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



