


import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

A      = 2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
A      = {
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
A      = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
    A      = "facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
    A      = "allenai"





def __UpperCAmelCase       (		__A			)						->					Any:



       '''simple docstring'''





       UpperCAmelCase__										=     dict((re.sub(R"@@$" ,						"" ,						__lowerCAmelCase			), v) if k.endswith("@@"			) else (re.sub(R"$" ,						"</w>" ,						__lowerCAmelCase			), v) for k, v in d.items()			)
       UpperCAmelCase__										=     """<s> <pad> </s> <unk>""".split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"""{k}</w>"""]
              UpperCAmelCase__										=     d[k]  # restore
       return da





def __UpperCAmelCase       (		__A ,						__A			)						->					Optional[int]:



       '''simple docstring'''





       assert os.path.exists(__lowerCAmelCase			)
       os.makedirs(__lowerCAmelCase ,						exist_ok=__lowerCAmelCase			)
       print(F"""Writing results to {pytorch_dump_folder_path}"""			)

       # handle various types of models

       UpperCAmelCase__										=     basename(__lowerCAmelCase			)
       UpperCAmelCase__										=     dirname(__lowerCAmelCase			)

       UpperCAmelCase__										=     fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
       UpperCAmelCase__										=     cls.hub_models()
       UpperCAmelCase__										=     {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
       UpperCAmelCase__										=     """."""
       # note: since the model dump is old, fairseq has upgraded its model some
       # time later, and it does a whole lot of rewrites and splits on the saved
       # weights, therefore we can't use torch.load() directly on the model file.
       # see: upgrade_state_dict(state_dict) in fairseq_model.py
       print(F"""using checkpoint {checkpoint_file}"""			)
       UpperCAmelCase__										=     hub_utils.from_pretrained(
           __lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						archive_map=__lowerCAmelCase ,						**__lowerCAmelCase			)

       UpperCAmelCase__										=     vars(chkpt["args"]["model"]			)

       UpperCAmelCase__										=     args["""source_lang"""]
       UpperCAmelCase__										=     args["""target_lang"""]

       UpperCAmelCase__										=     dirname(__lowerCAmelCase			)
       UpperCAmelCase__										=     basename(__lowerCAmelCase			)

       # dicts
       UpperCAmelCase__										=     os.path.join(__lowerCAmelCase ,						F"""dict.{src_lang}.txt"""			)
       UpperCAmelCase__										=     os.path.join(__lowerCAmelCase ,						F"""dict.{tgt_lang}.txt"""			)

       UpperCAmelCase__										=     Dictionary.load(__lowerCAmelCase			)
       UpperCAmelCase__										=     rewrite_dict_keys(src_dict.indices			)
       UpperCAmelCase__										=     len(__lowerCAmelCase			)
       UpperCAmelCase__										=     os.path.join(__lowerCAmelCase ,						"vocab-src.json"			)
       print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""			)
       with open(__lowerCAmelCase ,						"w" ,						encoding="utf-8"			) as f:
              f.write(json.dumps(__lowerCAmelCase ,						ensure_ascii=__lowerCAmelCase ,						indent=__lowerCAmelCase			)			)

       # detect whether this is a do_lower_case situation, which can be derived by checking whether we
       # have at least one uppercase letter in the source vocab
       UpperCAmelCase__										=     True
       for k in src_vocab.keys():
              if not k.islower():
                     UpperCAmelCase__										=     False
                     break

       UpperCAmelCase__										=     Dictionary.load(__lowerCAmelCase			)
       UpperCAmelCase__										=     rewrite_dict_keys(tgt_dict.indices			)
       UpperCAmelCase__										=     len(__lowerCAmelCase			)
       UpperCAmelCase__										=     os.path.join(__lowerCAmelCase ,						"vocab-tgt.json"			)
       print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""			)
       with open(__lowerCAmelCase ,						"w" ,						encoding="utf-8"			) as f:
              f.write(json.dumps(__lowerCAmelCase ,						ensure_ascii=__lowerCAmelCase ,						indent=__lowerCAmelCase			)			)

       # merges_file (bpecodes)
       UpperCAmelCase__										=     os.path.join(__lowerCAmelCase ,						VOCAB_FILES_NAMES["merges_file"]			)
       for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
              UpperCAmelCase__										=     os.path.join(__lowerCAmelCase ,						__lowerCAmelCase			)
              if os.path.exists(__lowerCAmelCase			):
                     break
       with open(__lowerCAmelCase ,						encoding="utf-8"			) as fin:
              UpperCAmelCase__										=     fin.read()
       UpperCAmelCase__										=     re.sub(R" \d+$" ,						"" ,						__lowerCAmelCase ,						0 ,						re.M			)  # remove frequency number
       print(F"""Generating {merges_file}"""			)
       with open(__lowerCAmelCase ,						"w" ,						encoding="utf-8"			) as fout:
              fout.write(__lowerCAmelCase			)

       # model config
       UpperCAmelCase__										=     os.path.join(__lowerCAmelCase ,						"config.json"			)

       # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
       # may have to modify the tokenizer if a different type is used by a future model
       assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args["bpe"]}"""
       assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args["tokenizer"]}"""

       UpperCAmelCase__										=     {
           """architectures""": ["""FSMTForConditionalGeneration"""],
           """model_type""": """fsmt""",
           """activation_dropout""": args["""activation_dropout"""],
           """activation_function""": """relu""",
           """attention_dropout""": args["""attention_dropout"""],
           """d_model""": args["""decoder_embed_dim"""],
           """dropout""": args["""dropout"""],
           """init_std""": 0.02,
           """max_position_embeddings""": args["""max_source_positions"""],
           """num_hidden_layers""": args["""encoder_layers"""],
           """src_vocab_size""": src_vocab_size,
           """tgt_vocab_size""": tgt_vocab_size,
           """langs""": [src_lang, tgt_lang],
           """encoder_attention_heads""": args["""encoder_attention_heads"""],
           """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
           """encoder_layerdrop""": args["""encoder_layerdrop"""],
           """encoder_layers""": args["""encoder_layers"""],
           """decoder_attention_heads""": args["""decoder_attention_heads"""],
           """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
           """decoder_layerdrop""": args["""decoder_layerdrop"""],
           """decoder_layers""": args["""decoder_layers"""],
           """bos_token_id""": 0,
           """pad_token_id""": 1,
           """eos_token_id""": 2,
           """is_encoder_decoder""": True,
           """scale_embedding""": not args["""no_scale_embedding"""],
           """tie_word_embeddings""": args["""share_all_embeddings"""],
       }

       # good hparam defaults to start with
       UpperCAmelCase__										=     5
       UpperCAmelCase__										=     False
       if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
              UpperCAmelCase__										=     best_score_hparams[model_dir]["""length_penalty"""]
       else:
              UpperCAmelCase__										=     1.0

       print(F"""Generating {fsmt_model_config_file}"""			)
       with open(__lowerCAmelCase ,						"w" ,						encoding="utf-8"			) as f:
              f.write(json.dumps(__lowerCAmelCase ,						ensure_ascii=__lowerCAmelCase ,						indent=__lowerCAmelCase			)			)

       # tokenizer config
       UpperCAmelCase__										=     os.path.join(__lowerCAmelCase ,						__lowerCAmelCase			)

       UpperCAmelCase__										=     {
           """langs""": [src_lang, tgt_lang],
           """model_max_length""": 1_0_2_4,
           """do_lower_case""": do_lower_case,
       }

       print(F"""Generating {fsmt_tokenizer_config_file}"""			)
       with open(__lowerCAmelCase ,						"w" ,						encoding="utf-8"			) as f:
              f.write(json.dumps(__lowerCAmelCase ,						ensure_ascii=__lowerCAmelCase ,						indent=__lowerCAmelCase			)			)

       # model
       UpperCAmelCase__										=     chkpt["""models"""][0]
       UpperCAmelCase__										=     model.state_dict()

       # rename keys to start with 'model.'
       UpperCAmelCase__										=     OrderedDict(("model." + k, v) for k, v in model_state_dict.items()			)

       # remove unneeded keys
       UpperCAmelCase__										=     [
           """model.model""",
           """model.encoder.version""",
           """model.decoder.version""",
           """model.encoder_embed_tokens.weight""",
           """model.decoder_embed_tokens.weight""",
           """model.encoder.embed_positions._float_tensor""",
           """model.decoder.embed_positions._float_tensor""",
       ]
       for k in ignore_keys:
              model_state_dict.pop(__lowerCAmelCase ,						__lowerCAmelCase			)

       UpperCAmelCase__										=     FSMTConfig.from_pretrained(__lowerCAmelCase			)
       UpperCAmelCase__										=     FSMTForConditionalGeneration(__lowerCAmelCase			)

       # check that it loads ok
       model_new.load_state_dict(__lowerCAmelCase ,						strict=__lowerCAmelCase			)

       # save
       UpperCAmelCase__										=     os.path.join(__lowerCAmelCase ,						__lowerCAmelCase			)
       print(F"""Generating {pytorch_weights_dump_path}"""			)
       torch.save(__lowerCAmelCase ,						__lowerCAmelCase			)

       print("Conversion is done!"			)
       print("\nLast step is to upload the files to s3"			)
       print(F"""cd {data_root}"""			)
       print(F"""transformers-cli upload {model_dir}"""			)


if __name__ == "__main__":
    A      = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--fsmt_checkpoint_path",
        default=None,
        type=str,
        required=True,
        help=(
            "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
            " bpecodes, etc."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    A      = parser.parse_args()
    convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)









from .imports import is_tqdm_available


if is_tqdm_available():
    from tqdm.auto import tqdm as _tqdm

from ..state import PartialState





def __UpperCAmelCase       (		__A = True ,						*__A ,						**__A			)						->					Any:



       '''simple docstring'''





       if not is_tqdm_available():
              raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."			)
       UpperCAmelCase__										=     False
       if main_process_only:
              UpperCAmelCase__										=     PartialState().local_process_index == 0
       return _tqdm(*__A ,						**__A ,						disable=__A			)





