





from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class     lowerCamelCase(	datasets.BuilderConfig  ):
     '''simple docstring'''


     __magic_name__        = None
     __magic_name__        = None
     __magic_name__        = None




     def 					lowerCAmelCase__		(						self       ):
         assert self.generator is not None, "generator must be specified"

         if self.gen_kwargs is None:
             _A    =       {}







class     lowerCamelCase(	datasets.GeneratorBasedBuilder  ):
     '''simple docstring'''


     __magic_name__        = GeneratorConfig




     def 					lowerCAmelCase__		(						self       ):
         return datasets.DatasetInfo(features=self.config.features       )




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,    gen_kwargs=self.config.gen_kwargs       )]




     def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
         for idx, ex in enumerate(self.config.generator(**snake_case_       )       ):
             yield idx, ex












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 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[str]:




    """simple docstring"""




    _A    =       FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE							)
    print(F"Building PyTorch model from configuration: {config}"							)
    _A    =       FNetForPreTraining(_SCREAMING_SNAKE_CASE							)

    _A    =       restore_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _A    =       checkpoint_dict['target']

    # Embeddings
    # Position IDs
    _A    =       fnet_pretraining_model.state_dict()

    _A    =       state_dict['fnet.embeddings.position_ids']
    _A    =       {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']							)

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

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']							).T
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']							).T
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']							)
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']							)

    # Pooler Layers
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']							).T
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['bias']							)

    # Masked LM Layers
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']							)
    _A    =       torch.tensor(pretrained_model_params['predictions_output']['output_bias']							)

    # Seq Relationship Layers
    _A    =       torch.tensor(
        pretrained_model_params['classification']['output_kernel']							)
    _A    =       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__":
 __A       : 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.")
 __A       : Any						    = parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





