
"""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						=    """hf-internal-testing/tiny-stable-diffusion-pipe"""
   __lowerCAmelCase						=    1
   __lowerCAmelCase						=    77
   __lowerCAmelCase						=    32
   __lowerCAmelCase						=    4
   __lowerCAmelCase						=    __lowerCAmelCase						=    64
   __lowerCAmelCase						=    torch.manual_seed(0						)
   def       __magic_name__      (      self     ,   _a=0.9_9_9_9    ):
          lowercase   :    str			=							UNetaDConditionModel.from_pretrained(self.model_id     ,   subfolder="unet"    )
          lowercase   :    int			=							unet.to(_a    )
          lowercase   :    List[str]			=							EMAModel(unet.parameters()     ,   decay=_a     ,   model_cls=_a     ,   model_config=unet.config    )
          return unet, ema_unet
   def       __magic_name__      (      self    ):
          lowercase   :    str			=							torch.randn(
              self.batch_size     ,   self.num_in_channels     ,   self.latent_height     ,   self.latent_width     ,   generator=self.generator    ).to(_a    )
          lowercase   :    Any			=							torch.randint(0     ,   1_000     ,   size=(self.batch_size,)     ,   generator=self.generator    ).to(_a    )
          lowercase   :    int			=							torch.randn(
              self.batch_size     ,   self.prompt_length     ,   self.text_encoder_hidden_dim     ,   generator=self.generator    ).to(_a    )
          return noisy_latents, timesteps, encoder_hidden_states
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Any			=							{}
          for k, param in unet.state_dict().items():
                 lowercase   :    int			=							torch.randn_like(_a    ) + (param * torch.randn_like(_a    ))
                 updated_state_dict.update({k: updated_param}    )
          unet.load_state_dict(_a    )
          return unet
   def       __magic_name__      (      self    ):
          lowercase   :    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       __magic_name__      (      self    ):
          lowercase   :    Optional[int]			=							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   :    int			=							list(unet.parameters()    )
          for s_param, param in zip(ema_unet.shadow_params     ,   _a    ):
                 assert torch.allclose(_a     ,   _a    )

          # 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     ,   _a    ):
                 assert torch.allclose(_a     ,   _a    )
   def       __magic_name__      (      self    ):
          lowercase   :    Union[str, 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.
          lowercase   :    List[Any]			=							self.simulate_backprop(_a    )

          # 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[str]			=							list(unet_pseudo_updated_step_one.parameters()    )
          for s_param, param in zip(ema_unet.shadow_params     ,   _a    ):
                 assert ~torch.allclose(_a     ,   _a    )

          # 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     ,   _a    ):
                 assert ~torch.allclose(_a     ,   _a    )
   def       __magic_name__      (      self    ):
          # If we call EMA step after a backpropagation consecutively for two times,
          # the shadow params from those two steps should be different.
          lowercase   :    str			=							self.get_models()

          # First backprop + EMA
          lowercase   :    Any			=							self.simulate_backprop(_a    )
          ema_unet.step(unet_step_one.parameters()    )
          lowercase   :    Optional[Any]			=							ema_unet.shadow_params

          # Second backprop + EMA
          lowercase   :    Dict			=							self.simulate_backprop(_a    )
          ema_unet.step(unet_step_two.parameters()    )
          lowercase   :    List[str]			=							ema_unet.shadow_params

          for step_one, step_two in zip(_a     ,   _a    ):
                 assert ~torch.allclose(_a     ,   _a    )
   def       __magic_name__      (      self    ):
          # If there's no decay even if there are backprops, EMA steps
          # won't take any effect i.e., the shadow params would remain the
          # same.
          lowercase   :    Optional[Any]			=							self.get_models(decay=0.0    )
          lowercase   :    Any			=							self.simulate_backprop(_a    )
          ema_unet.step(unet_step_one.parameters()    )
          lowercase   :    int			=							ema_unet.shadow_params

          lowercase   :    Tuple			=							self.simulate_backprop(_a    )
          ema_unet.step(unet_step_two.parameters()    )
          lowercase   :    Tuple			=							ema_unet.shadow_params

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

   @skip_mps
   def       __magic_name__      (      self    ):
          lowercase   :    Dict			=							self.get_models()
          lowercase   :    int			=							self.get_dummy_inputs()

          with tempfile.TemporaryDirectory() as tmpdir:
                 ema_unet.save_pretrained(_a    )
                 lowercase   :    int			=							UNetaDConditionModel.from_pretrained(_a     ,   model_cls=_a    )
                 lowercase   :    str			=							loaded_unet.to(unet.device    )

          # Since no EMA step has been performed the outputs should match.
          lowercase   :    Optional[int]			=							unet(_a     ,   _a     ,   _a    ).sample
          lowercase   :    List[Any]			=							loaded_unet(_a     ,   _a     ,   _a    ).sample

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




"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A      :	str       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """tvlt"""
   def __init__(      self     ,   _a=224     ,   _a=2_048     ,   _a=128     ,   _a=[16, 16]     ,   _a=[16, 16]     ,   _a=3     ,   _a=1     ,   _a=8     ,   _a=768     ,   _a=12     ,   _a=12     ,   _a=3_072     ,   _a="gelu"     ,   _a=0.0     ,   _a=0.0     ,   _a=0.0_2     ,   _a=1E-6     ,   _a=True     ,   _a=False     ,   _a=16     ,   _a=512     ,   _a=8     ,   _a=2_048     ,   _a=0.7_5     ,   _a=0.1_5     ,   _a="frame-level"     ,   _a=True     ,   _a=True     ,   _a="classification"     ,   **_a     ,   ):
          super().__init__(**_a    )

          if audio_mask_type not in ("frame-level", "patch_level"):
                 raise ValueError(
                     "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
                     f"""got {audio_mask_type}"""    )

          lowercase   :    Union[str, Any]			=							image_size
          lowercase   :    Dict			=							spectrogram_length
          lowercase   :    Union[str, Any]			=							frequency_length
          lowercase   :    Union[str, Any]			=							image_patch_size
          lowercase   :    Any			=							audio_patch_size
          lowercase   :    int			=							num_image_channels
          lowercase   :    Union[str, Any]			=							num_audio_channels
          lowercase   :    Any			=							num_frames

          lowercase   :    List[str]			=							hidden_size
          lowercase   :    List[str]			=							num_hidden_layers
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    int			=							intermediate_size
          lowercase   :    Tuple			=							hidden_act
          lowercase   :    int			=							hidden_dropout_prob
          lowercase   :    Dict			=							attention_probs_dropout_prob
          lowercase   :    List[Any]			=							initializer_range
          lowercase   :    Union[str, Any]			=							layer_norm_eps
          lowercase   :    Dict			=							qkv_bias
          lowercase   :    List[Any]			=							use_mean_pooling

          lowercase   :    List[Any]			=							decoder_num_attention_heads
          lowercase   :    Union[str, Any]			=							decoder_hidden_size
          lowercase   :    Dict			=							decoder_num_hidden_layers
          lowercase   :    Union[str, Any]			=							decoder_intermediate_size
          lowercase   :    List[str]			=							pixel_mask_ratio
          lowercase   :    str			=							audio_mask_ratio
          lowercase   :    Any			=							audio_mask_type

          lowercase   :    Union[str, Any]			=							task_matching
          lowercase   :    Optional[Any]			=							task_mae
          lowercase   :    List[str]			=							loss_type


