'''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    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]      ,		__snake_case       : List[str]      ,		__snake_case       : str							):
     # Initialise PyTorch model
     _A								=     FNetConfig.from_json_file(__snake_case							)
     print(F'Building PyTorch model from configuration: {config}'							)
     _A								=     FNetForPreTraining(__snake_case							)

     _A								=     restore_checkpoint(__snake_case      ,		__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(__snake_case							)

     # Save PreTrained
     print(F'Saving pretrained model to {save_path}'							)
     fnet_pretraining_model.save_pretrained(__snake_case							)


if __name__ == "__main__":
   _UpperCAmelCase						:    Optional[int]     					=					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.''')
   _UpperCAmelCase						:    Dict     					=					parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




'''simple docstring'''






import torch

from accelerate import Accelerator, DistributedType







class lowercase_					:







   """simple docstring"""

   def __enter__(			self			: Optional[int]    )     -> Union[str, Any]:
        if self.enabled:
             _A								=     self.model.no_sync()
             self.model_sync_obj.__enter__()

        return self
   def __exit__(			self			: Dict,   UpperCamelCase__			: Any,   UpperCamelCase__			: int,   UpperCamelCase__			: Any    )     -> str:
        if self.enabled:
             # Average all models on exit
             self._sync_and_avg_model_params()
             self.model_sync_obj.__exit__(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    )
   def __init__(			self			: Optional[int],   UpperCamelCase__			: Accelerator,   UpperCamelCase__			: torch.nn.Module,   UpperCamelCase__			: int,   UpperCamelCase__			: bool = True    )     -> str:
        if accelerator.distributed_type not in [
            DistributedType.NO,
            DistributedType.MULTI_CPU,
            DistributedType.MULTI_GPU,
        ]:
             raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'    )
        _A								=     enabled and accelerator.distributed_type != DistributedType.NO
        _A								=     0
        if self.enabled:
             _A								=     accelerator
             _A								=     model
             _A								=     local_sgd_steps
   def   __UpperCAmelCase      (			self			: Any    )     -> int:
        self.num_steps += 1
        if not self.enabled:
             return

        if self.num_steps % self.local_sgd_steps == 0:
             self._sync_and_avg_model_params()

   def   __UpperCAmelCase      (			self			: Dict    )     -> int:
        self.accelerator.wait_for_everyone()
        with self.accelerator.autocast():
             for param in self.model.parameters():
                  _A								=     self.accelerator.reduce(param.data,   reduction='mean'    )



