# 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()

a  :      List[str]            =    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  :      Dict            =    {
    # 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  :      List[str]            =    {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
 a  :      int            =    """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  :      Any            =    """allenai"""
def 		snake_case__	(		lowercase					):
   # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
   # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
   lowerCAmelCase_:							List[Any]       =	dict((re.sub(r"@@$"							,				""							,				lowercase					), v) if k.endswith("@@"					) else (re.sub(r"$"							,				"</w>"							,				lowercase					), v) for k, v in d.items()					)
   lowerCAmelCase_:							Optional[Any]       =	"<s> <pad> </s> <unk>".split()
   # restore the special tokens
   for k in keep_keys:
      del da[F'''{k}</w>''']
      lowerCAmelCase_:							Union[str, Any]       =	d[k]  # restore
   return da
def 		snake_case__	(		lowercase							,				lowercase					):
   # prep
   assert os.path.exists(lowercase					)
   os.makedirs(lowercase							,				exist_ok=lowercase					)
   print(F'''Writing results to {pytorch_dump_folder_path}'''					)

   # handle various types of models

   lowerCAmelCase_:							Tuple       =	basename(lowercase					)
   lowerCAmelCase_:							str       =	dirname(lowercase					)

   lowerCAmelCase_:							int       =	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
   lowerCAmelCase_:							List[str]       =	cls.hub_models()
   lowerCAmelCase_:							Tuple       =	{"bpe": "fastbpe", "tokenizer": "moses"}
   lowerCAmelCase_:							Optional[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_:							Tuple       =	hub_utils.from_pretrained(
       lowercase							,				lowercase							,				lowercase							,				archive_map=lowercase							,				**lowercase					)

   lowerCAmelCase_:							Tuple       =	vars(chkpt["args"]["model"]					)

   lowerCAmelCase_:							Tuple       =	args["source_lang"]
   lowerCAmelCase_:							str       =	args["target_lang"]

   lowerCAmelCase_:							int       =	dirname(lowercase					)
   lowerCAmelCase_:							List[str]       =	basename(lowercase					)

   # dicts
   lowerCAmelCase_:							Dict       =	os.path.join(lowercase							,				F'''dict.{src_lang}.txt'''					)
   lowerCAmelCase_:							int       =	os.path.join(lowercase							,				F'''dict.{tgt_lang}.txt'''					)

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

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

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

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

   # model config
   lowerCAmelCase_:							Any       =	os.path.join(lowercase							,				"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_:							Dict       =	{
       "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_:							int       =	5
   lowerCAmelCase_:							Optional[int]       =	False
   if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
      lowerCAmelCase_:							int       =	best_score_hparams[model_dir]["length_penalty"]
   else:
      lowerCAmelCase_:							List[Any]       =	1.0

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

   # tokenizer config
   lowerCAmelCase_:							List[Any]       =	os.path.join(lowercase							,				lowercase					)

   lowerCAmelCase_:							Optional[int]       =	{
       "langs": [src_lang, tgt_lang],
       "model_max_length": 1024,
       "do_lower_case": do_lower_case,
   }

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

   # model
   lowerCAmelCase_:							Any       =	chkpt["models"][0]
   lowerCAmelCase_:							Optional[Any]       =	model.state_dict()

   # rename keys to start with 'model.'
   lowerCAmelCase_:							Optional[Any]       =	OrderedDict(("model." + k, v) for k, v in model_state_dict.items()					)

   # remove unneeded keys
   lowerCAmelCase_:							Optional[int]       =	[
       "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(lowercase							,				lowercase					)

   lowerCAmelCase_:							Tuple       =	FSMTConfig.from_pretrained(lowercase					)
   lowerCAmelCase_:							List[str]       =	FSMTForConditionalGeneration(lowercase					)

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

   # save
   lowerCAmelCase_:							Optional[int]       =	os.path.join(lowercase							,				lowercase					)
   print(F'''Generating {pytorch_weights_dump_path}'''					)
   torch.save(lowercase							,				lowercase					)

   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  :      Dict            =    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  :      Any            =    parser.parse_args()
 convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self ):
       lowerCAmelCase_:							dict[str, TrieNode]       =	{}  # Mapping from char to TrieNode
       lowerCAmelCase_:							str       =	False




    def      _a    (    self       ,						lowerCamelCase__ ):
       for word in words:
          self.insert(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	self
       for char in word:
          if char not in curr.nodes:
             lowerCAmelCase_:							List[Any]       =	TrieNode()
          lowerCAmelCase_:							Optional[Any]       =	curr.nodes[char]
       lowerCAmelCase_:							Dict       =	True




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	self
       for char in word:
          if char not in curr.nodes:
             return False
          lowerCAmelCase_:							Dict       =	curr.nodes[char]
       return curr.is_leaf




    def      _a    (    self       ,						lowerCamelCase__ ):

       def _delete(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ) -> bool:
          if index == len(lowerCamelCase__ ):
             # If word does not exist
             if not curr.is_leaf:
                return False
             lowerCAmelCase_:							List[str]       =	False
             return len(curr.nodes ) == 0
          lowerCAmelCase_:							Union[str, Any]       =	word[index]
          lowerCAmelCase_:							Optional[int]       =	curr.nodes.get(lowerCamelCase__ )
          # If char not in current trie node
          if not char_node:
             return False
          # Flag to check if node can be deleted
          lowerCAmelCase_:							List[Any]       =	_delete(lowerCamelCase__       ,						lowerCamelCase__       ,						index + 1 )
          if delete_curr:
             del curr.nodes[char]
             return len(curr.nodes ) == 0
          return delete_curr

       _delete(self       ,						lowerCamelCase__       ,						0 )





def 		snake_case__	(		lowercase							,				lowercase					):
   if node.is_leaf:
      print(lowercase							,				end=" "					)

   for key, value in node.nodes.items():
      print_words(lowercase							,				word + key					)
def 		snake_case__	(		):
   lowerCAmelCase_:							Any       =	"banana bananas bandana band apple all beast".split()
   lowerCAmelCase_:							Optional[Any]       =	TrieNode()
   root.insert_many(lowercase					)
   # print_words(root, "")
   assert all(root.find(lowercase					) for word in words					)
   assert root.find("banana"					)
   assert not root.find("bandanas"					)
   assert not root.find("apps"					)
   assert root.find("apple"					)
   assert root.find("all"					)
   root.delete("all"					)
   assert not root.find("all"					)
   root.delete("banana"					)
   assert not root.find("banana"					)
   assert root.find("bananas"					)
   return True
def 		snake_case__	(		lowercase							,				lowercase					):
   print(str(lowercase					)							,				"works!" if passes else "doesn't work :("					)
def 		snake_case__	(		):
   assert test_trie()
def 		snake_case__	(		):
   print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
 main()