
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 			UpperCamelCase_				(  unittest.TestCase     ):




      '''simple docstring'''







      a		:List[str]						 =      'hf-internal-testing/tiny-stable-diffusion-pipe'
      a		:List[str]						 =      1
      a		:Tuple						 =      77
      a		:Union[str, Any]						 =      32
      a		:Any						 =      4
      a		:Union[str, Any]						 =      64
      a		:Any						 =      torch.manual_seed(0     )







      def lowercase__		( self					,					_UpperCAmelCase=0.9999):
            lowerCAmelCase_						=      UNetaDConditionModel.from_pretrained(self.model_id					,					subfolder='''unet''')
            lowerCAmelCase_						=      unet.to(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      EMAModel(unet.parameters()					,					decay=__SCREAMING_SNAKE_CASE					,					model_cls=__SCREAMING_SNAKE_CASE					,					model_config=unet.config)
            return unet, ema_unet







      def lowercase__		( self):
            lowerCAmelCase_						=      torch.randn(
                self.batch_size					,					self.num_in_channels					,					self.latent_height					,					self.latent_width					,					generator=self.generator).to(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      torch.randint(0					,					1_000					,					size=(self.batch_size,)					,					generator=self.generator).to(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      torch.randn(
                self.batch_size					,					self.prompt_length					,					self.text_encoder_hidden_dim					,					generator=self.generator).to(__SCREAMING_SNAKE_CASE)
            return noisy_latents, timesteps, encoder_hidden_states







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







      def lowercase__		( self):
            lowerCAmelCase_						=      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 lowercase__		( self):
            lowerCAmelCase_						=      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())
            lowerCAmelCase_						=      list(unet.parameters())
            for s_param, param in zip(ema_unet.shadow_params					,					__SCREAMING_SNAKE_CASE):
                  assert torch.allclose(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)

            # 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					,					__SCREAMING_SNAKE_CASE):
                  assert torch.allclose(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)







      def lowercase__		( self):
            lowerCAmelCase_						=      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.
            lowerCAmelCase_						=      self.simulate_backprop(__SCREAMING_SNAKE_CASE)

            # 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.
            lowerCAmelCase_						=      list(unet_pseudo_updated_step_one.parameters())
            for s_param, param in zip(ema_unet.shadow_params					,					__SCREAMING_SNAKE_CASE):
                  assert ~torch.allclose(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)

            # 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					,					__SCREAMING_SNAKE_CASE):
                  assert ~torch.allclose(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.get_models()

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

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

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







      def lowercase__		( self):
            lowerCAmelCase_						=      self.get_models(decay=0.0)
            lowerCAmelCase_						=      self.simulate_backprop(__SCREAMING_SNAKE_CASE)
            ema_unet.step(unet_step_one.parameters())
            lowerCAmelCase_						=      ema_unet.shadow_params

            lowerCAmelCase_						=      self.simulate_backprop(__SCREAMING_SNAKE_CASE)
            ema_unet.step(unet_step_two.parameters())
            lowerCAmelCase_						=      ema_unet.shadow_params

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







      @skip_mps
      def lowercase__		( self):
            lowerCAmelCase_						=      self.get_models()
            lowerCAmelCase_						=      self.get_dummy_inputs()

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

            # Since no EMA step has been performed the outputs should match.
            lowerCAmelCase_						=      unet(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE).sample
            lowerCAmelCase_						=      loaded_unet(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE).sample

            assert torch.allclose(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					atol=1E-4)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase      			=  {
    """configuration_switch_transformers""": [
        """SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwitchTransformersConfig""",
        """SwitchTransformersOnnxConfig""",
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SwitchTransformersEncoderModel""",
         """SwitchTransformersForConditionalGeneration""",
         """SwitchTransformersModel""",
         """SwitchTransformersPreTrainedModel""",
         """SwitchTransformersTop1Router""",
         """SwitchTransformersSparseMLP""",
     ]


if TYPE_CHECKING:
     from .configuration_switch_transformers import (
         SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwitchTransformersConfig,
         SwitchTransformersOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_switch_transformers import (
              SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwitchTransformersEncoderModel,
              SwitchTransformersForConditionalGeneration,
              SwitchTransformersModel,
              SwitchTransformersPreTrainedModel,
              SwitchTransformersSparseMLP,
              SwitchTransformersTopaRouter,
          )


else:
     import sys

     __UpperCAmelCase      			=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

