

# 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()

__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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    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   :					List[str]        =    "<s> <pad> </s> <unk>".split()
     # restore the special tokens
     for k in keep_keys:
          del da[f'''{k}</w>''']
          _lowerCAmelCase   :					Any        =    d[k]  # restore
     return da







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     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[str]        =    basename(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[str]        =    dirname(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					Tuple        =    fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
     _lowerCAmelCase   :					Optional[Any]        =    cls.hub_models()
     _lowerCAmelCase   :					int        =    {"bpe": "fastbpe", "tokenizer": "moses"}
     _lowerCAmelCase   :					Dict        =    "."
     # 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   :					str        =    hub_utils.from_pretrained(
         SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							archive_map=SCREAMING_SNAKE_CASE		,							**SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					int        =    vars(chkpt["args"]["model"]				)

     _lowerCAmelCase   :					Optional[int]        =    args["source_lang"]
     _lowerCAmelCase   :					Any        =    args["target_lang"]

     _lowerCAmelCase   :					Dict        =    dirname(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Dict        =    basename(SCREAMING_SNAKE_CASE				)

     # dicts
     _lowerCAmelCase   :					int        =    os.path.join(SCREAMING_SNAKE_CASE		,							f'''dict.{src_lang}.txt'''				)
     _lowerCAmelCase   :					Any        =    os.path.join(SCREAMING_SNAKE_CASE		,							f'''dict.{tgt_lang}.txt'''				)

     _lowerCAmelCase   :					int        =    Dictionary.load(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Any        =    rewrite_dict_keys(src_dict.indices				)
     _lowerCAmelCase   :					Dict        =    len(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Any        =    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   :					List[Any]        =    True
     for k in src_vocab.keys():
          if not k.islower():
               _lowerCAmelCase   :					Dict        =    False
               break

     _lowerCAmelCase   :					Union[str, Any]        =    Dictionary.load(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[Any]        =    rewrite_dict_keys(tgt_dict.indices				)
     _lowerCAmelCase   :					int        =    len(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[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   :					Optional[int]        =    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[int]        =    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   :					Any        =    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   :					Optional[int]        =    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   :					Tuple        =    {
         "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   :					str        =    5
     _lowerCAmelCase   :					int        =    False
     if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
          _lowerCAmelCase   :					Any        =    best_score_hparams[model_dir]["length_penalty"]
     else:
          _lowerCAmelCase   :					List[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   :					Tuple        =    os.path.join(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					List[Any]        =    {
         "langs": [src_lang, tgt_lang],
         "model_max_length": 1024,
         "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   :					Dict        =    chkpt["models"][0]
     _lowerCAmelCase   :					str        =    model.state_dict()

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

     # remove unneeded keys
     _lowerCAmelCase   :					Optional[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   :					str        =    FSMTConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Tuple        =    FSMTForConditionalGeneration(SCREAMING_SNAKE_CASE				)

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

     # save
     _lowerCAmelCase   :					List[Any]        =    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__":
  __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)









import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







     return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    {}

     for key, value in state_dict.items():
          if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
               continue

          _lowerCAmelCase   :					Union[str, Any]        =    key.replace("heads.cmd.mim_head.cls.predictions"		,							"mmm_image_head"				)
          _lowerCAmelCase   :					int        =    key.replace("heads.cmd.mlm_head.cls.predictions"		,							"mmm_text_head"				)
          _lowerCAmelCase   :					Any        =    key.replace("heads.cmd.itm_head.cls"		,							"itm_head"				)
          _lowerCAmelCase   :					Optional[Any]        =    key.replace("heads.cmd.itm_head.pooler"		,							"itm_head.pooler"				)
          _lowerCAmelCase   :					Any        =    key.replace("heads.cmd.clip_head.logit_scale"		,							"flava.logit_scale"				)
          _lowerCAmelCase   :					List[Any]        =    key.replace("heads.fairseq_mlm.cls.predictions"		,							"mlm_head"				)
          _lowerCAmelCase   :					Union[str, Any]        =    key.replace("heads.imagenet.mim_head.cls.predictions"		,							"mim_head"				)
          _lowerCAmelCase   :					Optional[int]        =    key.replace("mm_text_projection"		,							"flava.text_to_mm_projection"				)
          _lowerCAmelCase   :					int        =    key.replace("mm_image_projection"		,							"flava.image_to_mm_projection"				)
          _lowerCAmelCase   :					List[str]        =    key.replace("image_encoder.module"		,							"flava.image_model"				)
          _lowerCAmelCase   :					Optional[int]        =    key.replace("text_encoder.module"		,							"flava.text_model"				)
          _lowerCAmelCase   :					List[str]        =    key.replace("mm_encoder.module.encoder.cls_token"		,							"flava.multimodal_model.cls_token"				)
          _lowerCAmelCase   :					Dict        =    key.replace("mm_encoder.module"		,							"flava.multimodal_model"				)
          _lowerCAmelCase   :					Dict        =    key.replace("text_projection"		,							"flava.text_projection"				)
          _lowerCAmelCase   :					Optional[Any]        =    key.replace("image_projection"		,							"flava.image_projection"				)

          _lowerCAmelCase   :					List[Any]        =    value.float()

     for key, value in codebook_state_dict.items():
          _lowerCAmelCase   :					int        =    value

     return upgrade







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None				)       ->      Tuple:

     """simple docstring"""







     if config_path is not None:
          _lowerCAmelCase   :					Dict        =    FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
     else:
          _lowerCAmelCase   :					Dict        =    FlavaConfig()

     _lowerCAmelCase   :					List[Any]        =    FlavaForPreTraining(SCREAMING_SNAKE_CASE				).eval()

     _lowerCAmelCase   :					Optional[int]        =    convert_dalle_checkpoint(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							save_checkpoint=SCREAMING_SNAKE_CASE				)

     if os.path.exists(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Dict        =    torch.load(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)
     else:
          _lowerCAmelCase   :					Tuple        =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)

     _lowerCAmelCase   :					Optional[int]        =    upgrade_state_dict(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     hf_model.load_state_dict(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    hf_model.state_dict()
     _lowerCAmelCase   :					Optional[Any]        =    count_parameters(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    count_parameters(SCREAMING_SNAKE_CASE				) + count_parameters(SCREAMING_SNAKE_CASE				)

     assert torch.allclose(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							atol=1e-3				)

     hf_model.save_pretrained(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
  parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  __UpperCAmelCase    				=     parser.parse_args()

  convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






