




"""simple docstring"""







import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					(VQDiffusionScheduler,)







   def      UpperCAmelCase__ (		self					,				**lowerCamelCase_)							->      Optional[int]:
       UpperCamelCase										=  {
           '''num_vec_classes''': 4_0_9_7,
           '''num_train_timesteps''': 1_0_0,
       }

       config.update(**lowerCamelCase_)
       return config







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Tuple:
       UpperCamelCase										=  4
       UpperCamelCase										=  8
       UpperCamelCase										=  8

       UpperCamelCase										=  torch.randint(0					,				lowerCamelCase_					,				(batch_size, height * width))

       return sample







   @property
   def      UpperCAmelCase__ (		self)							->      int:
       assert False







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[int]:
       def model(lowerCamelCase_					,				lowerCamelCase_					,				*lowerCamelCase_):
           UpperCamelCase					,  UpperCamelCase										=  sample.shape
           UpperCamelCase										=  torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels))
           UpperCamelCase										=  F.log_softmax(logits.double()					,				dim=1).float()
           return return_value

       return model







   def      UpperCAmelCase__ (		self)							->      str:
       for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
           self.check_over_configs(num_vec_classes=lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       for t in [0, 5_0, 9_9]:
           self.check_over_forward(time_step=lowerCamelCase_)







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





"""simple docstring"""







import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  BertConfig.from_pretrained(
        '''bert-large-cased''' ,vocab_size=_lowercase ,max_position_embeddings=512 ,is_decoder=_lowercase ,add_cross_attention=_lowercase ,)
    UpperCamelCase										=  bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase										=  BertGenerationConfig(**_lowercase						)
    if is_encoder:
        UpperCamelCase										=  BertGenerationEncoder(_lowercase						)
    else:
        UpperCamelCase										=  BertGenerationDecoder(_lowercase						)
    print(f'Building PyTorch model from configuration: {config}'						)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _lowercase ,_lowercase ,model_class='''bert''' ,is_encoder_named_decoder=_lowercase ,is_encoder=_lowercase ,)

    # Save pytorch-model
    print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
    model.save_pretrained(_lowercase						)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_      =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--is_encoder_named_decoder',
      action='store_true',
      help='If decoder has to be renamed to encoder in PyTorch model.',
  )
  parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
  parser.add_argument('--vocab_size', default=50358, type=int, help='Vocab size of model')
  SCREAMING_SNAKE_CASE_      =					parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )