






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__			=   {
    "configuration_decision_transformer": [
        "DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DecisionTransformerConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCamelCase__			=   [
          "DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DecisionTransformerGPT2Model",
          "DecisionTransformerGPT2PreTrainedModel",
          "DecisionTransformerModel",
          "DecisionTransformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DecisionTransformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_decision_transformer import (
                DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DecisionTransformerGPTaModel,
                DecisionTransformerGPTaPreTrainedModel,
                DecisionTransformerModel,
                DecisionTransformerPreTrainedModel,
            )


else:
      import sys

      lowerCamelCase__			=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












"""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							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Dict    ):
  # Initialise PyTorch model
  A__											=							FNetConfig.from_json_file(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )
  A__											=							FNetForPreTraining(UpperCamelCase    )

  A__											=							restore_checkpoint(UpperCamelCase		,				UpperCamelCase    )
  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(UpperCamelCase    )

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


if __name__ == "__main__":
      lowerCamelCase__			=   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__			=   parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




