

import io
import json
import subprocess


A__        =			[
    ["en", "ru"],
    ["ru", "en"],
    ["en", "de"],
    ["de", "en"],
]

A__        =			8





def 							__a       (					__A ,       __A	)     ->   Optional[int]:
    SCREAMING_SNAKE_CASE__					     =						{}
    for src, tgt in pairs:
        SCREAMING_SNAKE_CASE__					     =						f"{src}-{tgt}"
        SCREAMING_SNAKE_CASE__					     =						f"sacrebleu -t wmt19 -l {pair} --echo src".split()
        SCREAMING_SNAKE_CASE__					     =						subprocess.run(snake_case__ ,       stdout=subprocess.PIPE	).stdout.decode("utf-8"	).splitlines()
        SCREAMING_SNAKE_CASE__					     =						f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
        SCREAMING_SNAKE_CASE__					     =						subprocess.run(snake_case__ ,       stdout=subprocess.PIPE	).stdout.decode("utf-8"	).splitlines()
        SCREAMING_SNAKE_CASE__					     =						{'src': src_lines[:n_objs], 'tgt': tgt_lines[:n_objs]}
    return text


A__        =			get_all_data(pairs, n_objs)
A__        =			"./fsmt_val_data.json"
with io.open(filename, "w", encoding="utf-8") as f:
  A__        =			json.dump(text, f, indent=2, ensure_ascii=False)





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       (					__A ,       __A ,       __A	):
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE__					     =						FNetConfig.from_json_file(__A	)
    print(f"Building PyTorch model from configuration: {config}"	)
    SCREAMING_SNAKE_CASE__					     =						FNetForPreTraining(__A	)

    SCREAMING_SNAKE_CASE__					     =						restore_checkpoint(__A ,       __A	)
    SCREAMING_SNAKE_CASE__					     =						checkpoint_dict["target"]

    # Embeddings
    # Position IDs
    SCREAMING_SNAKE_CASE__					     =						fnet_pretraining_model.state_dict()

    SCREAMING_SNAKE_CASE__					     =						state_dict["fnet.embeddings.position_ids"]
    SCREAMING_SNAKE_CASE__					     =						{"fnet.embeddings.position_ids": position_ids}
    # Embedding Layers
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]	).T
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]	)

    # Encoder Layers
    for layer in range(config.num_hidden_layers	):
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["scale"]	)
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["bias"]	)

        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]	).T
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]	)

        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"]	).T
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"]	)

        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["scale"]	)
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["bias"]	)

    # Pooler Layers
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]	).T
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]	)

    # Masked LM Layers
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_dense"]["kernel"]	).T
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_dense"]["bias"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_layer_norm"]["scale"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_layer_norm"]["bias"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["predictions_output"]["output_bias"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]	)

    # Seq Relationship Layers
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["classification"]["output_kernel"]	)
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["classification"]["output_bias"]	)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(__A	)

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


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


