'''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				):
     lowerCamelCase						:  str							="""hf-internal-testing/tiny-stable-diffusion-pipe"""
     lowerCamelCase						:  Optional[int]							=1
     lowerCamelCase						:  List[str]							=7_7
     lowerCamelCase						:  int							=3_2
     lowerCamelCase						:  Dict							=4
     lowerCamelCase						:  str							=6_4
     lowerCamelCase						:  Optional[Any]							=torch.manual_seed(0				)






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[str]						,       a	:	Optional[Any]=0.99_99					):





         """simple docstring"""



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






     def      SCREAMING_SNAKE_CASE__     (				self	:	Union[str, Any]					):





         """simple docstring"""



         __lowerCamelCase						=						torch.randn(
             self.batch_size						,       self.num_in_channels						,       self.latent_height						,       self.latent_width						,       generator=self.generator					).to(lowerCAmelCase__					)
         __lowerCamelCase						=						torch.randint(0						,       10_00						,       size=(self.batch_size,)						,       generator=self.generator					).to(lowerCAmelCase__					)
         __lowerCamelCase						=						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      SCREAMING_SNAKE_CASE__     (				self	:	Union[str, Any]						,       a	:	Optional[Any]					):





         """simple docstring"""



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






     def      SCREAMING_SNAKE_CASE__     (				self	:	Tuple					):





         """simple docstring"""



         __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      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]					):





         """simple docstring"""



         __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						,       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      SCREAMING_SNAKE_CASE__     (				self	:	str					):





         """simple docstring"""



         __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(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.
         __lowerCamelCase						=						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      SCREAMING_SNAKE_CASE__     (				self	:	Union[str, Any]					):





         """simple docstring"""



         __lowerCamelCase						=						self.get_models()

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

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

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






     def      SCREAMING_SNAKE_CASE__     (				self	:	Tuple					):





         """simple docstring"""



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

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

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






     @skip_mps
     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]					):





         """simple docstring"""



         __lowerCamelCase						=						self.get_models()
         __lowerCamelCase						=						self.get_dummy_inputs()

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

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


__UpperCAmelCase  							={
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase  							=[
      "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RwkvForCausalLM",
      "RwkvModel",
      "RwkvPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rwkv import (
        RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
        RwkvForCausalLM,
        RwkvModel,
        RwkvPreTrainedModel,
    )
else:
  import sys

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