

import io
import json
import subprocess


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

UpperCamelCase__     :					Optional[int]								=			8


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Optional[int]       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    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(_SCREAMING_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(_SCREAMING_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


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





import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  json.load(open(_SCREAMING_SNAKE_CASE       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoConfig(
        hidden_size=config_json['n_embd']       ,      num_layers=config_json['n_layer']       ,      num_heads=config_json['n_head']       ,      attention_types=config_json['attention_types']       ,      max_position_embeddings=config_json['n_positions']       ,      resid_dropout=config_json['res_dropout']       ,      embed_dropout=config_json['embed_dropout']       ,      attention_dropout=config_json['attn_dropout']       ,      )
    print(f"""Building PyTorch model from configuration: {config}"""				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoForCausalLM(_SCREAMING_SNAKE_CASE				)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
    model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
       UpperCamelCase__     :					Tuple								=			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(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help=(
               "The config json file corresponding to the pre-trained mesh-tf 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."
       )
       UpperCamelCase__     :					Any								=			parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


