"""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  a__     ( unittest.TestCase						):
   snake_case_				=   "hf-internal-testing/tiny-stable-diffusion-pipe"
   snake_case_				=   1
   snake_case_				=   77
   snake_case_				=   32
   snake_case_				=   4
   snake_case_				=   snake_case_				=   64
   snake_case_				=   torch.manual_seed(0						)


   def 						snake_case__						(  self,					_UpperCAmelCase=0.9_999	):
        '''simple docstring'''



        lowercase__								=				UNetaDConditionModel.from_pretrained(self.model_id,					subfolder="unet"	)
        lowercase__								=				unet.to(__lowerCamelCase	)
        lowercase__								=				EMAModel(unet.parameters(),					decay=__lowerCamelCase,					model_cls=__lowerCamelCase,					model_config=unet.config	)
        return unet, ema_unet


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				torch.randn(
            self.batch_size,					self.num_in_channels,					self.latent_height,					self.latent_width,					generator=self.generator	).to(__lowerCamelCase	)
        lowercase__								=				torch.randint(0,					1000,					size=(self.batch_size,),					generator=self.generator	).to(__lowerCamelCase	)
        lowercase__								=				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 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				{}
        for k, param in unet.state_dict().items():
             lowercase__								=				torch.randn_like(__lowerCamelCase	) + (param * torch.randn_like(__lowerCamelCase	))
             updated_state_dict.update({k: updated_param}	)
        unet.load_state_dict(__lowerCamelCase	)
        return unet


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				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 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				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()	)
        lowercase__								=				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 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				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.
        lowercase__								=				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.
        lowercase__								=				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 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.get_models()

        # First backprop + EMA
        lowercase__								=				self.simulate_backprop(__lowerCamelCase	)
        ema_unet.step(unet_step_one.parameters()	)
        lowercase__								=				ema_unet.shadow_params

        # Second backprop + EMA
        lowercase__								=				self.simulate_backprop(__lowerCamelCase	)
        ema_unet.step(unet_step_two.parameters()	)
        lowercase__								=				ema_unet.shadow_params

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


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.get_models(decay=0.0	)
        lowercase__								=				self.simulate_backprop(__lowerCamelCase	)
        ema_unet.step(unet_step_one.parameters()	)
        lowercase__								=				ema_unet.shadow_params

        lowercase__								=				self.simulate_backprop(__lowerCamelCase	)
        ema_unet.step(unet_step_two.parameters()	)
        lowercase__								=				ema_unet.shadow_params

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


   @skip_mps
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.get_models()
        lowercase__								=				self.get_dummy_inputs()

        with tempfile.TemporaryDirectory() as tmpdir:
             ema_unet.save_pretrained(__lowerCamelCase	)
             lowercase__								=				UNetaDConditionModel.from_pretrained(__lowerCamelCase,					model_cls=__lowerCamelCase	)
             lowercase__								=				loaded_unet.to(unet.device	)

        # Since no EMA step has been performed the outputs should match.
        lowercase__								=				unet(__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase	).sample
        lowercase__								=				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__)



