



# 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  :				Optional[Any]					=					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  :				int					=					{
    # 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
__lowerCamelCase  :				Dict					=					{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		__lowerCamelCase  :				Optional[Any]					=					"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  :				int					=					"allenai"

def  lowerCamelCase_(lowerCamelCase_     )			->							Any:
						# (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}
						UpperCAmelCase								=  dict((re.sub(r"@@$"     , ""     , a_     ), v) if k.endswith("@@"     ) else (re.sub(r"$"     , "</w>"     , a_     ), 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  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     )			->							Any:
						# prep
						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

						UpperCAmelCase								=  basename(a_     )
						UpperCAmelCase								=  dirname(a_     )

						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(
						    a_     , a_     , a_     , archive_map=a_     , **a_     )

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

						UpperCAmelCase								=  args["source_lang"]
						UpperCAmelCase								=  args["target_lang"]

						UpperCAmelCase								=  dirname(a_     )
						UpperCAmelCase								=  basename(a_     )

						# dicts
						UpperCAmelCase								=  os.path.join(a_     , F'dict.{src_lang}.txt'     )
						UpperCAmelCase								=  os.path.join(a_     , F'dict.{tgt_lang}.txt'     )

						UpperCAmelCase								=  Dictionary.load(a_     )
						UpperCAmelCase								=  rewrite_dict_keys(src_dict.indices     )
						UpperCAmelCase								=  len(a_     )
						UpperCAmelCase								=  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
						UpperCAmelCase								=  True
						for k in src_vocab.keys():
												if not k.islower():
																		UpperCAmelCase								=  False
																		break

						UpperCAmelCase								=  Dictionary.load(a_     )
						UpperCAmelCase								=  rewrite_dict_keys(tgt_dict.indices     )
						UpperCAmelCase								=  len(a_     )
						UpperCAmelCase								=  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)
						UpperCAmelCase								=  os.path.join(a_     , VOCAB_FILES_NAMES["merges_file"]     )
						for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
												UpperCAmelCase								=  os.path.join(a_     , a_     )
												if os.path.exists(a_     ):
																		break
						with open(a_     , encoding="utf-8"     ) as fin:
												UpperCAmelCase								=  fin.read()
						UpperCAmelCase								=  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
						UpperCAmelCase								=  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"]}'

						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(a_     , "w"     , encoding="utf-8"     ) as f:
												f.write(json.dumps(a_     , ensure_ascii=a_     , indent=a_     )     )

						# tokenizer config
						UpperCAmelCase								=  os.path.join(a_     , a_     )

						UpperCAmelCase								=  {
						    "langs": [src_lang, tgt_lang],
						    "model_max_length": 1_024,
						    "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
						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(a_     , a_     )

						UpperCAmelCase								=  FSMTConfig.from_pretrained(a_     )
						UpperCAmelCase								=  FSMTForConditionalGeneration(a_     )

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

						# save
						UpperCAmelCase								=  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  :				List[Any]					=					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  :				List[str]					=					parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)











class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       Tuple:
								snake_case_           =				n
								snake_case_           =				[None] * self.n
								snake_case_           =				0  # index of the first element
								snake_case_           =				0
								snake_case_           =				0




	def __len__(			self   )			->       int:
								return self.size




	def 				_UpperCamelCase     (			self   )			->       bool:
								return self.size == 0




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								return False if self.is_empty() else self.array[self.front]




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if self.size >= self.n:
															raise Exception('QUEUE IS FULL'   )

								snake_case_           =				data
								snake_case_           =				(self.rear + 1) % self.n
								self.size += 1
								return self






	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								if self.size == 0:
															raise Exception('UNDERFLOW'   )

								snake_case_           =				self.array[self.front]
								snake_case_           =				None
								snake_case_           =				(self.front + 1) % self.n
								self.size -= 1
								return temp





