


"""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()

UpperCAmelCase__  							=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
UpperCAmelCase__  							={
    # 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
UpperCAmelCase__  							={}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
       UpperCAmelCase__  							="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",
]:
       UpperCAmelCase__  							="allenai"


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: int ):






 """simple docstring"""







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

 # handle various types of models

 __lowercase									=	basename(UpperCamelCase__ )
 __lowercase									=	dirname(UpperCamelCase__ )

 __lowercase									=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
 __lowercase									=	cls.hub_models()
 __lowercase									=	{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
 __lowercase									=	"""."""
 # 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}''' )
 __lowercase									=	hub_utils.from_pretrained(
     UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__				,   archive_map=UpperCamelCase__				,   **UpperCamelCase__ )

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

 __lowercase									=	args["""source_lang"""]
 __lowercase									=	args["""target_lang"""]

 __lowercase									=	dirname(UpperCamelCase__ )
 __lowercase									=	basename(UpperCamelCase__ )

 # dicts
 __lowercase									=	os.path.join(UpperCamelCase__				,   f'''dict.{src_lang}.txt''' )
 __lowercase									=	os.path.join(UpperCamelCase__				,   f'''dict.{tgt_lang}.txt''' )

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

 # 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
 __lowercase									=	True
 for k in src_vocab.keys():
  if not k.islower():
   __lowercase									=	False
   break

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

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

 # model config
 __lowercase									=	os.path.join(UpperCamelCase__				,   """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']}'''

 __lowercase									=	{
     """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
 __lowercase									=	5
 __lowercase									=	False
 if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
  __lowercase									=	best_score_hparams[model_dir]["""length_penalty"""]
 else:
  __lowercase									=	1.0

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

 # tokenizer config
 __lowercase									=	os.path.join(UpperCamelCase__				,   UpperCamelCase__ )

 __lowercase									=	{
     """langs""": [src_lang, tgt_lang],
     """model_max_length""": 1024,
     """do_lower_case""": do_lower_case,
 }

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

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

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

 # remove unneeded keys
 __lowercase									=	[
     """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(UpperCamelCase__				,   UpperCamelCase__ )

 __lowercase									=	FSMTConfig.from_pretrained(UpperCamelCase__ )
 __lowercase									=	FSMTForConditionalGeneration(UpperCamelCase__ )

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

 # save
 __lowercase									=	os.path.join(UpperCamelCase__				,   UpperCamelCase__ )
 print(f'''Generating {pytorch_weights_dump_path}''' )
 torch.save(UpperCamelCase__				,   UpperCamelCase__ )

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




"""simple docstring"""

from typing import List

from .keymap import KEYMAP, get_character


def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += [key]
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator


def lowerCAmelCase_      (				*UpperCamelCase__	: List[str] ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += keys
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator





class    lowerCamelCase__						(		_a    ):
      def __new__(		cls	:	str      ,       A_	:	Optional[Any]      ,       A_	:	Union[str, Any]      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	super().__new__(cls      ,       A_      ,       A_      ,       A_							)
       if not hasattr(A_      ,       """key_handler"""							):
        setattr(A_      ,       """key_handler"""      ,       {}							)
       setattr(A_      ,       """handle_input"""      ,       KeyHandler.handle_input							)

       for value in attrs.values():
        __lowercase									=	getattr(A_      ,       """handle_key"""      ,       []							)
        for key in handled_keys:
         __lowercase									=	value
       return new_cls






      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	Dict							):






       '''simple docstring'''


       __lowercase									=	get_character()
       if char != KEYMAP["undefined"]:
        __lowercase									=	ord(A_							)
       __lowercase									=	cls.key_handler.get(A_							)
       if handler:
        __lowercase									=	char
        return handler(cls							)
       else:
        return None






def lowerCAmelCase_      (				cls	: int ):






 """simple docstring"""







 return KeyHandler(cls.__name__				,   cls.__bases__				,   cls.__dict__.copy() )
