



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"""



 lowerCamelCase_          =			'''hf-internal-testing/tiny-stable-diffusion-pipe'''
 lowerCamelCase_          =			1
 lowerCamelCase_          =			7_7
 lowerCamelCase_          =			3_2
 lowerCamelCase_          =			4
 lowerCamelCase_          =			lowerCamelCase_          =			6_4
 lowerCamelCase_          =			torch.manual_seed(0				)
 def lowercase_		(						self	,    a=0.9_999					)	->							Optional[Any]:




   """simple docstring"""







   _A           =    UNetaDConditionModel.from_pretrained(self.model_id	,    subfolder='''unet'''					)
   _A           =    unet.to(a					)
   _A           =    EMAModel(unet.parameters()	,    decay=a	,    model_cls=a	,    model_config=unet.config					)
   return unet, ema_unet
 def lowercase_		(						self					)	->							Union[str, Any]:




   """simple docstring"""







   _A           =    torch.randn(
       self.batch_size	,    self.num_in_channels	,    self.latent_height	,    self.latent_width	,    generator=self.generator					).to(a					)
   _A           =    torch.randint(0	,    1_0_0_0	,    size=(self.batch_size,)	,    generator=self.generator					).to(a					)
   _A           =    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 lowercase_		(						self	,    a					)	->							int:




   """simple docstring"""







   _A           =    {}
   for k, param in unet.state_dict().items():
     _A           =    torch.randn_like(a					) + (param * torch.randn_like(a					))
     updated_state_dict.update({k: updated_param}					)
   unet.load_state_dict(a					)
   return unet
 def lowercase_		(						self					)	->							Any:




   """simple docstring"""







   _A    ,   _A           =    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					)	->							Union[str, Any]:




   """simple docstring"""







   _A    ,   _A           =    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           =    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 lowercase_		(						self					)	->							Any:




   """simple docstring"""







   _A    ,   _A           =    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           =    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.
   _A           =    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 lowercase_		(						self					)	->							Any:




   """simple docstring"""







   _A    ,   _A           =    self.get_models()

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

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

   for step_one, step_two in zip(a	,    a					):
     assert ~torch.allclose(a	,    a					)
 def lowercase_		(						self					)	->							List[Any]:




   """simple docstring"""







   _A    ,   _A           =    self.get_models(decay=0.0					)
   _A           =    self.simulate_backprop(a					)
   ema_unet.step(unet_step_one.parameters()					)
   _A           =    ema_unet.shadow_params

   _A           =    self.simulate_backprop(a					)
   ema_unet.step(unet_step_two.parameters()					)
   _A           =    ema_unet.shadow_params

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



 @skip_mps
 def lowercase_		(						self					)	->							int:




   """simple docstring"""







   _A    ,   _A           =    self.get_models()
   _A    ,   _A    ,   _A           =    self.get_dummy_inputs()

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

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

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




import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Tuple:
  # Initialise PyTorch model
  _A           =    ElectraConfig.from_json_file(__snake_case							)
  print(F'''Building PyTorch model from configuration: {config}'''							)

  if discriminator_or_generator == "discriminator":
    _A           =    ElectraForPreTraining(__snake_case							)
  elif discriminator_or_generator == "generator":
    _A           =    ElectraForMaskedLM(__snake_case							)
  else:
    raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''							)

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      __snake_case						,				__snake_case						,				__snake_case						,				discriminator_or_generator=__snake_case							)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
  torch.save(model.state_dict()						,				__snake_case							)


if __name__ == "__main__":
    __lowerCamelCase  				=					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--discriminator_or_generator""",
        default=None,
        type=str,
        required=True,
        help=(
            """Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or """
            """'generator'."""
        ),
    )
    __lowerCamelCase  				=					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
    )