


"""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		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    # Initialise PyTorch model
    a											=FNetConfig.from_json_file(lowercase						)
    print(f'''Building PyTorch model from configuration: {config}'''						)
    a											=FNetForPreTraining(lowercase						)

    a											=restore_checkpoint(lowercase			,       lowercase						)
    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(lowercase						)

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


if __name__ == "__main__":
       lowerCamelCase_  :  Any     		=    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_  :  List[Any]     		=    parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class  __A							(			_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @register_to_config
 def __init__(						self					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A = False					,       )   ->	Optional[Any]:
     super().__init__()

     a											=nn.Linear(__A					,       __A					,       bias=__A		)

     a											=nn.Embedding(__A					,       __A		)
     a											=False

     a											=nn.Dropout(p=__A		)

     a											=TaConfig(
         d_model=__A					,       num_heads=__A					,       d_kv=__A					,       d_ff=__A					,       feed_forward_proj=__A					,       dropout_rate=__A					,       is_decoder=__A					,       is_encoder_decoder=__A					,       )
     a											=nn.ModuleList()
     for lyr_num in range(__A		):
         a											=TaBlock(__A		)
         self.encoders.append(__A		)

     a											=TaLayerNorm(__A		)
     a											=nn.Dropout(p=__A		)





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     a											=self.input_proj(__A		)

     # terminal relative positional encodings
     a											=encoder_inputs.shape[1]
     a											=torch.arange(__A					,       device=encoder_inputs.device		)

     a											=encoder_inputs_mask.sum(-1		)
     a											=torch.roll(input_positions.unsqueeze(0		)					,       tuple(seq_lens.tolist()		)					,       dims=0		)
     x += self.position_encoding(__A		)

     a											=self.dropout_pre(__A		)

     # inverted the attention mask
     a											=encoder_inputs.size()
     a											=self.get_extended_attention_mask(__A					,       __A		)

     for lyr in self.encoders:
         a											=lyr(__A					,       __A		)[0]
     a											=self.layer_norm(__A		)

     return self.dropout_post(__A		), encoder_inputs_mask