



import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


a__    =	logging.get_logger(__name__)
logging.set_verbosity_info()



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     Union[str, Any]:
 if "xprophetnet" in prophetnet_checkpoint_path:
  _snake_case    :  Dict					=					XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  List[str]					=					XLMProphetNetForConditionalGeneration.from_pretrained(
      SCREAMING_SNAKE_CASE__				,       output_loading_info=SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  List[Any]					=					ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Union[str, Any]					=					ProphetNetForConditionalGeneration.from_pretrained(
      SCREAMING_SNAKE_CASE__				,       output_loading_info=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Dict					=					["""key_proj""", """value_proj""", """query_proj"""]

 _snake_case    :  Optional[int]					=					{
     """self_attn""": """ngram_self_attn""",
     """cross_attn""": """encoder_attn""",
     """cross_attn_layer_norm""": """encoder_attn_layer_norm""",
     """feed_forward_layer_norm""": """final_layer_norm""",
     """feed_forward""": """""",
     """intermediate""": """fc1""",
     """output""": """fc2""",
     """key_proj""": """k_proj""",
     """query_proj""": """q_proj""",
     """value_proj""": """v_proj""",
     """word_embeddings""": """embed_tokens""",
     """embeddings_layer_norm""": """emb_layer_norm""",
     """relative_pos_embeddings""": """relative_linear""",
     """ngram_embeddings""": """ngram_input_embed""",
     """position_embeddings""": """embed_positions""",
 }

 for key in loading_info["missing_keys"]:
  _snake_case    :  int					=					key.split("""."""				)

  if attributes[0] == "lm_head":
   _snake_case    :  List[Any]					=					prophet
   _snake_case    :  Dict					=					prophet_old
  else:
   _snake_case    :  Any					=					prophet.prophetnet
   _snake_case    :  Union[str, Any]					=					prophet_old.model

  _snake_case    :  int					=					False
  for attribute in attributes:
   if attribute in mapping:
    _snake_case    :  List[Any]					=					mapping[attribute]
    if not hasattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) and len(SCREAMING_SNAKE_CASE__				) > 0:
     _snake_case    :  str					=					attribute
   elif hasattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
    _snake_case    :  Optional[Any]					=					attribute

   if attribute == "weight":
    assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
    _snake_case    :  Optional[int]					=					old_model.weight
    logger.info(F'''{attribute} is initialized.'''				)
    _snake_case    :  Optional[int]					=					True
    break
   elif attribute == "bias":
    assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
    _snake_case    :  Union[str, Any]					=					old_model.bias
    logger.info(F'''{attribute} is initialized'''				)
    _snake_case    :  Optional[Any]					=					True
    break
   elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE__				,       """in_proj_weight"""				):
    _snake_case    :  Optional[int]					=					old_model.in_proj_weight.shape[0] // 3
    _snake_case    :  int					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
    param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
    param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
    if attribute == "query_proj":
     _snake_case    :  List[str]					=					nn.Parameter(old_model.in_proj_weight[:embed_dim, :]				)
     _snake_case    :  int					=					nn.Parameter(old_model.in_proj_bias[:embed_dim]				)

    elif attribute == "key_proj":
     _snake_case    :  Optional[int]					=					nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]				)
     _snake_case    :  List[str]					=					nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]				)
    elif attribute == "value_proj":
     _snake_case    :  List[Any]					=					nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]				)
     _snake_case    :  int					=					nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]				)
    _snake_case    :  List[Any]					=					True
    break
   elif attribute == "position_embeddings":
    assert (
        model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
    ), "Hidden size has to match"
    assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
    _snake_case    :  Tuple					=					nn.Parameter(old_model.embed_positions.weight[:512, :]				)
    _snake_case    :  Optional[int]					=					True
    break

   if attribute.isdigit():
    _snake_case    :  Any					=					model[int(SCREAMING_SNAKE_CASE__				)]
    _snake_case    :  Any					=					old_model[int(SCREAMING_SNAKE_CASE__				)]
   else:
    _snake_case    :  List[str]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

    if old_attribute == "":
     _snake_case    :  List[str]					=					old_model
    else:
     if not hasattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
      raise ValueError(F'''{old_model} does not have {old_attribute}'''				)
     _snake_case    :  Union[str, Any]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

  if not is_key_init:
   raise ValueError(F'''{key} was not correctly initialized!'''				)

 print(F'''Saving model to {pytorch_dump_folder_path}'''				)
 prophet.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  a__    =	parser.parse_args()
  convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








# 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()

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     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}
 _snake_case    :  Union[str, Any]					=					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()				)
 _snake_case    :  int					=					"""<s> <pad> </s> <unk>""".split()
 # restore the special tokens
 for k in keep_keys:
  del da[F'''{k}</w>''']
  _snake_case    :  Tuple					=					d[k]  # restore
 return da



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     str:
 # prep
 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

 _snake_case    :  Optional[Any]					=					basename(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					dirname(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
 _snake_case    :  List[str]					=					cls.hub_models()
 _snake_case    :  Tuple					=					{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
 _snake_case    :  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}'''				)
 _snake_case    :  List[Any]					=					hub_utils.from_pretrained(
     SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       archive_map=SCREAMING_SNAKE_CASE__				,       **SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Dict					=					vars(chkpt["""args"""]["""model"""]				)

 _snake_case    :  Union[str, Any]					=					args["""source_lang"""]
 _snake_case    :  Tuple					=					args["""target_lang"""]

 _snake_case    :  Any					=					dirname(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					basename(SCREAMING_SNAKE_CASE__				)

 # dicts
 _snake_case    :  List[str]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dict.{src_lang}.txt'''				)
 _snake_case    :  Any					=					os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dict.{tgt_lang}.txt'''				)

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

 _snake_case    :  Union[str, Any]					=					Dictionary.load(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Union[str, Any]					=					rewrite_dict_keys(tgt_dict.indices				)
 _snake_case    :  Union[str, Any]					=					len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Tuple					=					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)
 _snake_case    :  str					=					os.path.join(SCREAMING_SNAKE_CASE__				,       VOCAB_FILES_NAMES["""merges_file"""]				)
 for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
  _snake_case    :  Union[str, Any]					=					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:
  _snake_case    :  Dict					=					fin.read()
 _snake_case    :  Optional[Any]					=					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
 _snake_case    :  List[str]					=					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']}'''

 _snake_case    :  Optional[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""": 0.0_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
 _snake_case    :  Tuple					=					5
 _snake_case    :  int					=					False
 if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
  _snake_case    :  List[str]					=					best_score_hparams[model_dir]["""length_penalty"""]
 else:
  _snake_case    :  Optional[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
 _snake_case    :  List[Any]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  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(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
 _snake_case    :  Optional[Any]					=					chkpt["""models"""][0]
 _snake_case    :  List[str]					=					model.state_dict()

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

 # remove unneeded keys
 _snake_case    :  Union[str, 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(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[int]					=					FSMTConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[Any]					=					FSMTForConditionalGeneration(SCREAMING_SNAKE_CASE__				)

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

 # save
 _snake_case    :  int					=					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__":
  a__    =	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."""
  )
  a__    =	parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
