




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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str     ):
					# (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 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					# 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''': 10_24,
					    '''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__    			=			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__    			=			parser.parse_args()
					convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''
import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 			lowerCamelCase_   (	unittest.TestCase      ):






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          debug_launcher(test_script.main )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          debug_launcher(test_ops.main )
