


'''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()

__lowercase      :		Tuple			=      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
__lowercase      :		List[Any]			=      {
    # 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
__lowercase      :		int			=      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	__lowercase      :		Union[str, 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",
]:
	__lowercase      :		Union[str, Any]			=      'allenai'






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Tuple  ):
					# (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     :			Tuple          =    dict((re.sub(r'@@$'		, ''		, UpperCamelCase_  ), v) if k.endswith('@@'  ) else (re.sub(r'$'		, '</w>'		, UpperCamelCase_  ), v) for k, v in d.items()  )
					__a     :			Optional[int]          =    '''<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     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Any		, _SCREAMING_SNAKE_CASE :  Optional[int]  ):
					# prep
					assert os.path.exists(UpperCamelCase_  )
					os.makedirs(UpperCamelCase_		, exist_ok=UpperCamelCase_  )
					print(F"""Writing results to {pytorch_dump_folder_path}"""  )

					# handle various types of models

					__a     :			int          =    basename(UpperCamelCase_  )
					__a     :			Dict          =    dirname(UpperCamelCase_  )

					__a     :			Tuple          =    fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
					__a     :			List[str]          =    cls.hub_models()
					__a     :			Dict          =    {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
					__a     :			Optional[Any]          =    '''.'''
					# 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(
					    UpperCamelCase_		, UpperCamelCase_		, UpperCamelCase_		, archive_map=UpperCamelCase_		, **UpperCamelCase_  )

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

					__a     :			Tuple          =    args['''source_lang''']
					__a     :			Any          =    args['''target_lang''']

					__a     :			Optional[int]          =    dirname(UpperCamelCase_  )
					__a     :			List[Any]          =    basename(UpperCamelCase_  )

					# dicts
					__a     :			Dict          =    os.path.join(UpperCamelCase_		, F"""dict.{src_lang}.txt"""  )
					__a     :			Tuple          =    os.path.join(UpperCamelCase_		, F"""dict.{tgt_lang}.txt"""  )

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

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

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

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

					# model config
					__a     :			Dict          =    os.path.join(UpperCamelCase_		, '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     :			Dict          =    {
					    '''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
					__a     :			Optional[int]          =    5
					__a     :			Tuple          =    False
					if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
										__a     :			List[Any]          =    best_score_hparams[model_dir]['''length_penalty''']
					else:
										__a     :			Dict          =    1.0

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

					# tokenizer config
					__a     :			Tuple          =    os.path.join(UpperCamelCase_		, UpperCamelCase_  )

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

					# model
					__a     :			Union[str, Any]          =    chkpt['''models'''][0]
					__a     :			int          =    model.state_dict()

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

					# remove unneeded keys
					__a     :			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(UpperCamelCase_		, UpperCamelCase_  )

					__a     :			int          =    FSMTConfig.from_pretrained(UpperCamelCase_  )
					__a     :			Tuple          =    FSMTForConditionalGeneration(UpperCamelCase_  )

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

					# save
					__a     :			Optional[Any]          =    os.path.join(UpperCamelCase_		, UpperCamelCase_  )
					print(F"""Generating {pytorch_weights_dump_path}"""  )
					torch.save(UpperCamelCase_		, UpperCamelCase_  )

					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__":
	__lowercase      :		Dict			=      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.'
	)
	__lowercase      :		Tuple			=      parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   encoder_layers=args.encoder_layers,   decoder_layers=args.decoder_layers,   encoder_attention_heads=args.encoder_attention_heads,   decoder_attention_heads=args.decoder_attention_heads,   encoder_ffn_dim=args.encoder_ffn_embed_dim,   decoder_ffn_dim=args.decoder_ffn_embed_dim,   d_model=args.encoder_embed_dim,   encoder_layerdrop=args.encoder_layerdrop,   decoder_layerdrop=args.decoder_layerdrop,   dropout=args.dropout,   attention_dropout=args.attention_dropout,   activation_dropout=args.activation_dropout,   activation_function='''relu''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
    __lowerCamelCase												=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    __lowerCamelCase												=						parser.parse_args()
    __lowerCamelCase												=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



