




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			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_		):
    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__":
 SCREAMING_SNAKE_CASE_    :       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.''')
 SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)







"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				list[int]						)    ->     tuple[int, int]:







     '''simple docstring'''


     if len(_SCREAMING_SNAKE_CASE						) == 1:
          return a[0], a[0]
     else:
          # split A into half.
          __A					:					List[str]								=		a[: len(_SCREAMING_SNAKE_CASE						) // 2]
          __A					:					Optional[Any]								=		a[len(_SCREAMING_SNAKE_CASE						) // 2 :]

          # 2 sub problems, 1/2 of original size.
          __A						,		__A					:					int								=		max_difference(_SCREAMING_SNAKE_CASE						)
          __A						,		__A					:					str								=		max_difference(_SCREAMING_SNAKE_CASE						)

          # get min of first and max of second
          # linear time
          __A					:					str								=		min(_SCREAMING_SNAKE_CASE						)
          __A					:					Optional[int]								=		max(_SCREAMING_SNAKE_CASE						)

          # 3 cases, either (small1, big1),
          # (min_first, max_second), (small2, big2)
          # constant comparisons
          if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
               return smalla, biga
          elif biga - smalla > max_second - min_first:
               return smalla, biga
          else:
               return min_first, max_second


if __name__ == "__main__":
       import doctest

       doctest.testmod()



