


'''simple docstring'''

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				: List[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
_UpperCAmelCase				: 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
_UpperCAmelCase				: Dict							    =				{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	_UpperCAmelCase				: str							    =				"""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				: Dict							    =				"""allenai"""

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

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						# prep
						assert os.path.exists(lowerCamelCase)
						os.makedirs(lowerCamelCase,				exist_ok=lowerCamelCase)
						print(F"""Writing results to {pytorch_dump_folder_path}""")

						# handle various types of models

						__lowerCAmelCase							       =				basename(lowerCamelCase)
						__lowerCAmelCase							       =				dirname(lowerCamelCase)

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

						__lowerCAmelCase							       =				vars(chkpt['''args''']['''model'''])

						__lowerCAmelCase							       =				args['''source_lang''']
						__lowerCAmelCase							       =				args['''target_lang''']

						__lowerCAmelCase							       =				dirname(lowerCamelCase)
						__lowerCAmelCase							       =				basename(lowerCamelCase)

						# dicts
						__lowerCAmelCase							       =				os.path.join(lowerCamelCase,				F"""dict.{src_lang}.txt""")
						__lowerCAmelCase							       =				os.path.join(lowerCamelCase,				F"""dict.{tgt_lang}.txt""")

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

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

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

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

						# model config
						__lowerCAmelCase							       =				os.path.join(lowerCamelCase,				'''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							       =				{
						    '''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							       =				5
						__lowerCAmelCase							       =				False
						if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
												__lowerCAmelCase							       =				best_score_hparams[model_dir]['''length_penalty''']
						else:
												__lowerCAmelCase							       =				1.0

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

						# tokenizer config
						__lowerCAmelCase							       =				os.path.join(lowerCamelCase,				lowerCamelCase)

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

						# model
						__lowerCAmelCase							       =				chkpt['''models'''][0]
						__lowerCAmelCase							       =				model.state_dict()

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

						# remove unneeded keys
						__lowerCAmelCase							       =				[
						    '''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(lowerCamelCase,				lowerCamelCase)

						__lowerCAmelCase							       =				FSMTConfig.from_pretrained(lowerCamelCase)
						__lowerCAmelCase							       =				FSMTForConditionalGeneration(lowerCamelCase)

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

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

						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				: 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."""
	)
	_UpperCAmelCase				: Optional[int]							    =				parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''

from torch import nn

def 							__magic_name__(						lowerCamelCase):
						if act_fn in ["swish", "silu"]:
												return nn.SiLU()
						elif act_fn == "mish":
												return nn.Mish()
						elif act_fn == "gelu":
												return nn.GELU()
						else:
												raise ValueError(F"""Unsupported activation function: {act_fn}""")

