



'''simple docstring'''





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     (    a_      ,		a_      ,		a_      ,		a_		)   ->       int:



   """simple docstring"""






   A_  :		Any						=					ElectraConfig.from_json_file(a_		)
   print(F"Building PyTorch model from configuration: {config}"		)

   if discriminator_or_generator == "discriminator":
      A_  :		Optional[Any]						=					ElectraForPreTraining(a_		)
   elif discriminator_or_generator == "generator":
      A_  :		List[Any]						=					ElectraForMaskedLM(a_		)
   else:
      raise ValueError("""The discriminator_or_generator argument should be either 'discriminator' or 'generator'"""		)

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

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


if __name__ == "__main__":
  UpperCamelCase__    :     Union[str, Any]			  =   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\'.'
      ),
  )
  UpperCamelCase__    :     str			  =   parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
  )





'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
