"""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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				List[Any]  ,       _SCREAMING_SNAKE_CASE				:				Dict						)    ->     Optional[int]:







     '''simple docstring'''


     __A					:					Union[str, Any]								=		FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE						)
     print(F'Building PyTorch model from configuration: {config}'						)
     __A					:					str								=		FNetForPreTraining(_SCREAMING_SNAKE_CASE						)

     __A					:					Any								=		restore_checkpoint(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)
     __A					:					List[str]								=		checkpoint_dict['target']

     # Embeddings
     # Position IDs
     __A					:					Dict								=		fnet_pretraining_model.state_dict()

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

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

          __A					:					str								=		torch.tensor(
              pretrained_model_params['encoder'][F'feed_forward_{layer}']['intermediate']['kernel']						).T
          __A					:					Union[str, Any]								=		torch.tensor(
              pretrained_model_params['encoder'][F'feed_forward_{layer}']['intermediate']['bias']						)

          __A					:					Dict								=		torch.tensor(
              pretrained_model_params['encoder'][F'feed_forward_{layer}']['output']['kernel']						).T
          __A					:					Tuple								=		torch.tensor(
              pretrained_model_params['encoder'][F'feed_forward_{layer}']['output']['bias']						)

          __A					:					str								=		torch.tensor(
              pretrained_model_params['encoder'][F'encoder_{layer}']['output_layer_norm']['scale']						)
          __A					:					Union[str, Any]								=		torch.tensor(
              pretrained_model_params['encoder'][F'encoder_{layer}']['output_layer_norm']['bias']						)

     # Pooler Layers
     __A					:					Union[str, Any]								=		torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']						).T
     __A					:					str								=		torch.tensor(pretrained_model_params['encoder']['pooler']['bias']						)

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

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




"""simple docstring"""







import pytest

from datasets.utils.version import Version




@pytest.mark.parametrize(
    'other, expected_equality'  ,       [
        (Version('1.0.0'						), True),
        ('1.0.0', True),
        (Version('2.0.0'						), False),
        ('2.0.0', False),
        ('1', False),
        ('a', False),
        (1, False),
        (None, False),
    ]  ,       )
def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     List[str]:







     '''simple docstring'''


     __A					:					Tuple								=		Version('1.0.0'						)
     assert (version == other) is expected_equality
     assert (version != other) is not expected_equality
     assert (hash(_SCREAMING_SNAKE_CASE						) == hash(_SCREAMING_SNAKE_CASE						)) is expected_equality



