

"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_					)
      print(f"Building PyTorch model from configuration: {config}"					)
      SCREAMING_SNAKE_CASE_			:					str          =       FNetForPreTraining(SCREAMING_SNAKE_CASE_					)

      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       restore_checkpoint(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_					)
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       checkpoint_dict["target"]

      # Embeddings
      # Position IDs
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       fnet_pretraining_model.state_dict()

      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       state_dict["fnet.embeddings.position_ids"]
      SCREAMING_SNAKE_CASE_			:					List[str]          =       {"fnet.embeddings.position_ids": position_ids}
      # Embedding Layers
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       torch.tensor(
          pretrained_model_params['encoder']['embedder']['word']['embedding']					)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       torch.tensor(
          pretrained_model_params['encoder']['embedder']['position']['embedding'][0]					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       torch.tensor(
          pretrained_model_params['encoder']['embedder']['type']['embedding']					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       torch.tensor(
          pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']					).T
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.tensor(
          pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']					)
      SCREAMING_SNAKE_CASE_			:					Dict          =       torch.tensor(
          pretrained_model_params['encoder']['embedder']['layer_norm']['scale']					)
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       torch.tensor(
          pretrained_model_params['encoder']['embedder']['layer_norm']['bias']					)

      # Encoder Layers
      for layer in range(config.num_hidden_layers					):
            SCREAMING_SNAKE_CASE_			:					Tuple          =       torch.tensor(
                pretrained_model_params['encoder'][f"encoder_{layer}"]['mixing_layer_norm']['scale']					)
            SCREAMING_SNAKE_CASE_			:					Any          =       torch.tensor(
                pretrained_model_params['encoder'][f"encoder_{layer}"]['mixing_layer_norm']['bias']					)

            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.tensor(
                pretrained_model_params['encoder'][f"feed_forward_{layer}"]['intermediate']['kernel']					).T
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.tensor(
                pretrained_model_params['encoder'][f"feed_forward_{layer}"]['intermediate']['bias']					)

            SCREAMING_SNAKE_CASE_			:					str          =       torch.tensor(
                pretrained_model_params['encoder'][f"feed_forward_{layer}"]['output']['kernel']					).T
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       torch.tensor(
                pretrained_model_params['encoder'][f"feed_forward_{layer}"]['output']['bias']					)

            SCREAMING_SNAKE_CASE_			:					str          =       torch.tensor(
                pretrained_model_params['encoder'][f"encoder_{layer}"]['output_layer_norm']['scale']					)
            SCREAMING_SNAKE_CASE_			:					str          =       torch.tensor(
                pretrained_model_params['encoder'][f"encoder_{layer}"]['output_layer_norm']['bias']					)

      # Pooler Layers
      SCREAMING_SNAKE_CASE_			:					Tuple          =       torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']					).T
      SCREAMING_SNAKE_CASE_			:					int          =       torch.tensor(pretrained_model_params['encoder']['pooler']['bias']					)

      # Masked LM Layers
      SCREAMING_SNAKE_CASE_			:					str          =       torch.tensor(
          pretrained_model_params['predictions_dense']['kernel']					).T
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       torch.tensor(
          pretrained_model_params['predictions_dense']['bias']					)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       torch.tensor(
          pretrained_model_params['predictions_layer_norm']['scale']					)
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       torch.tensor(
          pretrained_model_params['predictions_layer_norm']['bias']					)
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       torch.tensor(
          pretrained_model_params['encoder']['embedder']['word']['embedding']					)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       torch.tensor(
          pretrained_model_params['predictions_output']['output_bias']					)
      SCREAMING_SNAKE_CASE_			:					int          =       torch.tensor(pretrained_model_params['predictions_output']['output_bias']					)

      # Seq Relationship Layers
      SCREAMING_SNAKE_CASE_			:					int          =       torch.tensor(
          pretrained_model_params['classification']['output_kernel']					)
      SCREAMING_SNAKE_CASE_			:					str          =       torch.tensor(
          pretrained_model_params['classification']['output_bias']					)

      # Load State Dict
      fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE_					)

      # Save PreTrained
      print(f"Saving pretrained model to {save_path}"					)
      fnet_pretraining_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
    lowerCAmelCase       :				List[str]         =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--fnet_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained FNet model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
    lowerCAmelCase       :				str         =					parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)









from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase       :				Tuple         =					{'tokenization_phobert': ['PhobertTokenizer']}


if TYPE_CHECKING:
    from .tokenization_phobert import PhobertTokenizer

else:
    import sys

    lowerCAmelCase       :				Optional[int]         =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






