
import io
import json
import subprocess


lowerCamelCase_				:							Any       =    [
    ["en", "ru"],
    ["ru", "en"],
    ["en", "de"],
    ["de", "en"],
]

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


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


import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[Any]       =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
