






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Union[List[PIL.Image.Image], np.ndarray]
 _SCREAMING_SNAKE_CASE							:			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline












import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(__A		,		__A		,		__A )

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__A ) )
    torch.save(model.state_dict()		,		__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						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(
      '--rembert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained RemBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




