



"""simple docstring"""






# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

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()

lowerCAmelCase__      =    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
lowerCAmelCase__      =    {
    # fairseq:
    '''wmt19-ru-en''': {'''length_penalty''': 1.1},
    '''wmt19-en-ru''': {'''length_penalty''': 1.1_5},
    '''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
lowerCAmelCase__      =    {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
   lowerCAmelCase__      =    '''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",
]:
   lowerCAmelCase__      =    '''allenai'''






def 	snake_case_  (				A_ :		List[Any]						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		List[Any],    A_ :		Union[str, Any]						):


       '''simple docstring'''





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

       # handle various types of models

       _lowerCamelCase						:    Optional[int]         =			basename(A_						)
       _lowerCamelCase						:    int         =			dirname(A_						)

       _lowerCamelCase						:    Any         =			fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
       _lowerCamelCase						:    Any         =			cls.hub_models()
       _lowerCamelCase						:    Optional[Any]         =			{'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
       _lowerCamelCase						:    int         =			'''.'''
       # 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}'''						)
       _lowerCamelCase						:    Dict         =			hub_utils.from_pretrained(
           A_,    A_,    A_,    archive_map=A_,    **A_						)

       _lowerCamelCase						:    Optional[Any]         =			vars(chkpt['''args''']['''model''']						)

       _lowerCamelCase						:    List[Any]         =			args['''source_lang''']
       _lowerCamelCase						:    Dict         =			args['''target_lang''']

       _lowerCamelCase						:    int         =			dirname(A_						)
       _lowerCamelCase						:    str         =			basename(A_						)

       # dicts
       _lowerCamelCase						:    Union[str, Any]         =			os.path.join(A_,    F'''dict.{src_lang}.txt'''						)
       _lowerCamelCase						:    Dict         =			os.path.join(A_,    F'''dict.{tgt_lang}.txt'''						)

       _lowerCamelCase						:    Union[str, Any]         =			Dictionary.load(A_						)
       _lowerCamelCase						:    Any         =			rewrite_dict_keys(src_dict.indices						)
       _lowerCamelCase						:    int         =			len(A_						)
       _lowerCamelCase						:    Optional[int]         =			os.path.join(A_,    '''vocab-src.json'''						)
       print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''						)
       with open(A_,    '''w''',    encoding='''utf-8'''						) as f:
              f.write(json.dumps(A_,    ensure_ascii=A_,    indent=A_						)						)

       # 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
       _lowerCamelCase						:    List[str]         =			True
       for k in src_vocab.keys():
              if not k.islower():
                     _lowerCamelCase						:    Tuple         =			False
                     break

       _lowerCamelCase						:    Optional[int]         =			Dictionary.load(A_						)
       _lowerCamelCase						:    Optional[Any]         =			rewrite_dict_keys(tgt_dict.indices						)
       _lowerCamelCase						:    Optional[Any]         =			len(A_						)
       _lowerCamelCase						:    int         =			os.path.join(A_,    '''vocab-tgt.json'''						)
       print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''						)
       with open(A_,    '''w''',    encoding='''utf-8'''						) as f:
              f.write(json.dumps(A_,    ensure_ascii=A_,    indent=A_						)						)

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

       # model config
       _lowerCamelCase						:    str         =			os.path.join(A_,    '''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"]}'''

       _lowerCamelCase						:    Optional[int]         =			{
           '''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
       _lowerCamelCase						:    Tuple         =			5
       _lowerCamelCase						:    Dict         =			False
       if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
              _lowerCamelCase						:    Any         =			best_score_hparams[model_dir]['''length_penalty''']
       else:
              _lowerCamelCase						:    Optional[int]         =			1.0

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

       # tokenizer config
       _lowerCamelCase						:    Dict         =			os.path.join(A_,    A_						)

       _lowerCamelCase						:    List[str]         =			{
           '''langs''': [src_lang, tgt_lang],
           '''model_max_length''': 10_24,
           '''do_lower_case''': do_lower_case,
       }

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

       # model
       _lowerCamelCase						:    str         =			chkpt['''models'''][0]
       _lowerCamelCase						:    List[Any]         =			model.state_dict()

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

       # remove unneeded keys
       _lowerCamelCase						:    str         =			[
           '''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(A_,    A_						)

       _lowerCamelCase						:    int         =			FSMTConfig.from_pretrained(A_						)
       _lowerCamelCase						:    List[str]         =			FSMTForConditionalGeneration(A_						)

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

       # save
       _lowerCamelCase						:    Any         =			os.path.join(A_,    A_						)
       print(F'''Generating {pytorch_weights_dump_path}'''						)
       torch.save(A_,    A_						)

       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__":
   lowerCAmelCase__      =    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.'''
   )
   lowerCAmelCase__      =    parser.parse_args()
   convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""






import math






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       return math.sqrt(A_						) * math.sqrt(A_						) == num






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			0
       _lowerCamelCase						:    Tuple         =			n
       while left <= right:
              _lowerCamelCase						:    List[str]         =			(left + right) // 2
              if mid**2 == n:
                     return True
              elif mid**2 > n:
                     _lowerCamelCase						:    int         =			mid - 1
              else:
                     _lowerCamelCase						:    Optional[Any]         =			mid + 1
       return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()


