






'''simple docstring'''







from __future__ import annotations

__lowerCAmelCase									=   10





def 		_UpperCAmelCase (				__A				: list[int]   ):
				a_		:	str									=   1
				a_		:	List[Any]									=   max(__A   )
				while placement <= max_digit:
								# declare and initialize empty buckets
								a_		:	list[list]									=   [[] for _ in range(__A   )]
								# split list_of_ints between the buckets
								for i in list_of_ints:
												a_		:	Any									=   int((i / placement) % RADIX   )
												buckets[tmp].append(__A   )
								# put each buckets' contents into list_of_ints
								a_		:	Optional[int]									=   0
								for b in range(__A   ):
												for i in buckets[b]:
																a_		:	Dict									=   i
																a += 1
        # move to next
								placement *= RADIX
				return list_of_ints


if __name__ == "__main__":
		import doctest

		doctest.testmod()












'''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()

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





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





def 		_UpperCAmelCase (				__A				: List[Any]    ,		__A				: List[str]   ):
				# 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

				a_		:	Union[str, Any]									=   basename(__A   )
				a_		:	Optional[Any]									=   dirname(__A   )

				a_		:	List[Any]									=   fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
				a_		:	str									=   cls.hub_models()
				a_		:	List[str]									=   {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
				a_		:	str									=   '''.'''
				# 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}'   )
				a_		:	Any									=   hub_utils.from_pretrained(
				    __A    ,		__A    ,		__A    ,		archive_map=__A    ,		**__A   )

				a_		:	Optional[int]									=   vars(chkpt['''args''']['''model''']   )

				a_		:	Any									=   args['''source_lang''']
				a_		:	List[Any]									=   args['''target_lang''']

				a_		:	Union[str, Any]									=   dirname(__A   )
				a_		:	int									=   basename(__A   )

				# dicts
				a_		:	Optional[Any]									=   os.path.join(__A    ,		f'dict.{src_lang}.txt'   )
				a_		:	int									=   os.path.join(__A    ,		f'dict.{tgt_lang}.txt'   )

				a_		:	Any									=   Dictionary.load(__A   )
				a_		:	Any									=   rewrite_dict_keys(src_dict.indices   )
				a_		:	List[Any]									=   len(__A   )
				a_		:	Optional[Any]									=   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
				a_		:	Tuple									=   True
				for k in src_vocab.keys():
								if not k.islower():
												a_		:	Dict									=   False
												break

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

				a_		:	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''': 0.02,
				    '''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
				a_		:	List[Any]									=   5
				a_		:	Tuple									=   False
				if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
								a_		:	Optional[int]									=   best_score_hparams[model_dir]['''length_penalty''']
				else:
								a_		:	Union[str, Any]									=   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
				a_		:	Dict									=   os.path.join(__A    ,		__A   )

				a_		:	List[str]									=   {
				    '''langs''': [src_lang, tgt_lang],
				    '''model_max_length''': 10_24,
				    '''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
				a_		:	Any									=   chkpt['''models'''][0]
				a_		:	Optional[int]									=   model.state_dict()

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

				# remove unneeded keys
				a_		:	Optional[Any]									=   [
				    '''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   )

				a_		:	str									=   FSMTConfig.from_pretrained(__A   )
				a_		:	Optional[int]									=   FSMTForConditionalGeneration(__A   )

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

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




