





# 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()

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


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Optional[int]:
		# (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'''@@$'''							,       ''''''							,       SCREAMING_SNAKE_CASE_		), v) if k.endswith('''@@'''		) else (re.sub(R'''$'''							,       '''</w>'''							,       SCREAMING_SNAKE_CASE_		), v) for k, v in d.items()		)
		lowerCamelCase   : Tuple								='''<s> <pad> </s> <unk>'''.split()
		# restore the special tokens
		for k in keep_keys:
				del da[F"{k}</w>"]
				lowerCamelCase   : Optional[Any]								=d[k]  # restore
		return da


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Optional[Any]:
		# prep
		assert os.path.exists(SCREAMING_SNAKE_CASE_		)
		os.makedirs(SCREAMING_SNAKE_CASE_							,       exist_ok=SCREAMING_SNAKE_CASE_		)
		print(F"Writing results to {pytorch_dump_folder_path}"		)

		# handle various types of models

		lowerCamelCase   : List[Any]								=basename(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Union[str, Any]								=dirname(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : Tuple								=fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
		lowerCamelCase   : Union[str, Any]								=cls.hub_models()
		lowerCamelCase   : Any								={'''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   : Union[str, Any]								=hub_utils.from_pretrained(
		    SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       archive_map=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_		)

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

		lowerCamelCase   : Union[str, Any]								=args['''source_lang''']
		lowerCamelCase   : Optional[int]								=args['''target_lang''']

		lowerCamelCase   : Optional[Any]								=dirname(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Tuple								=basename(SCREAMING_SNAKE_CASE_		)

		# dicts
		lowerCamelCase   : Optional[Any]								=os.path.join(SCREAMING_SNAKE_CASE_							,       F"dict.{src_lang}.txt"		)
		lowerCamelCase   : int								=os.path.join(SCREAMING_SNAKE_CASE_							,       F"dict.{tgt_lang}.txt"		)

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

		# 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   : Union[str, Any]								=True
		for k in src_vocab.keys():
				if not k.islower():
						lowerCamelCase   : Optional[Any]								=False
						break

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

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

		# model config
		lowerCamelCase   : Tuple								=os.path.join(SCREAMING_SNAKE_CASE_							,       '''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''': 0.0_2,
		    '''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   : Dict								=5
		lowerCamelCase   : List[Any]								=False
		if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
				lowerCamelCase   : str								=best_score_hparams[model_dir]['''length_penalty''']
		else:
				lowerCamelCase   : Optional[Any]								=1.0

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

		# tokenizer config
		lowerCamelCase   : Optional[Any]								=os.path.join(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : str								={
		    '''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(SCREAMING_SNAKE_CASE_							,       '''w'''							,       encoding='''utf-8'''		) as f:
				f.write(json.dumps(SCREAMING_SNAKE_CASE_							,       ensure_ascii=SCREAMING_SNAKE_CASE_							,       indent=SCREAMING_SNAKE_CASE_		)		)

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

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

		# remove unneeded keys
		lowerCamelCase   : 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(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : Any								=FSMTConfig.from_pretrained(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Optional[Any]								=FSMTForConditionalGeneration(SCREAMING_SNAKE_CASE_		)

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

		# save
		lowerCamelCase   : int								=os.path.join(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		print(F"Generating {pytorch_weights_dump_path}"		)
		torch.save(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)

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








def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = False		)   ->     bool:
		if n == 2:
				return True
		if not n % 2 or n < 2:
				return False
		if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
				return False
		if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
				raise ValueError(
				    '''Warning: upper bound of deterministic test is exceeded. '''
				    '''Pass allow_probable=True to allow probabilistic test. '''
				    '''A return value of True indicates a probable prime.'''		)
		# array bounds provided by analysis
		lowerCamelCase   : Optional[Any]								=[
		    2_0_4_7,
		    1_3_7_3_6_5_3,
		    2_5_3_2_6_0_0_1,
		    3_2_1_5_0_3_1_7_5_1,
		    2_1_5_2_3_0_2_8_9_8_7_4_7,
		    3_4_7_4_7_4_9_6_6_0_3_8_3,
		    3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
		    1,
		    3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
		    1,
		    1,
		    3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
		    3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
		]

		lowerCamelCase   : Dict								=[2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
		for idx, _p in enumerate(SCREAMING_SNAKE_CASE_							,       1		):
				if n < _p:
						# then we have our last prime to check
						lowerCamelCase   : Any								=primes[:idx]
						break
		lowerCamelCase		,   lowerCamelCase   : Any								=n - 1, 0
		# break up n -1 into a power of 2 (s) and
		# remaining odd component
		# essentially, solve for d * 2 ** s == n - 1
		while d % 2 == 0:
				d //= 2
				s += 1
		for prime in plist:
				lowerCamelCase   : Union[str, Any]								=False
				for r in range(SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : List[str]								=pow(SCREAMING_SNAKE_CASE_							,       d * 2**r							,       SCREAMING_SNAKE_CASE_		)
						# see article for analysis explanation for m
						if (r == 0 and m == 1) or ((m + 1) % n == 0):
								lowerCamelCase   : List[str]								=True
								# this loop will not determine compositeness
								break
				if pr:
						continue
				# if pr is False, then the above loop never evaluated to true,
				# and the n MUST be composite
				return False
		return True


def 					A__	(       )   ->     None:
		assert not miller_rabin(5_6_1		)
		assert miller_rabin(5_6_3		)
		# 2047

		assert not miller_rabin(8_3_8_2_0_1		)
		assert miller_rabin(8_3_8_2_0_7		)
		# 1_373_653

		assert not miller_rabin(1_7_3_1_6_0_0_1		)
		assert miller_rabin(1_7_3_1_6_0_1_7		)
		# 25_326_001

		assert not miller_rabin(3_0_7_8_3_8_6_6_4_1		)
		assert miller_rabin(3_0_7_8_3_8_6_6_5_3		)
		# 3_215_031_751

		assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1		)
		assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9		)
		# 2_152_302_898_747

		assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7		)
		assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7		)
		# 3_474_749_660_383

		assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1		)
		assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7		)
		# 341_550_071_728_321

		assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1		)
		assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1		)
		# 3_825_123_056_546_413_051

		assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7		)
		assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1		)
		# 318_665_857_834_031_151_167_461

		assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3		)
		assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9		)
		# 3_317_044_064_679_887_385_961_981
		# upper limit for probabilistic test


if __name__ == "__main__":
				test_miller_rabin()

