



import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


a__    =	{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

a__    =	{
    """169M""": 7_68,
    """430M""": 10_24,
    """1B5""": 20_48,
    """3B""": 25_60,
    """7B""": 40_96,
    """14B""": 51_20,
}



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				)  ->     int:
 _snake_case    :  Dict					=					list(state_dict.keys()				)
 for name in state_dict_keys:
  _snake_case    :  Any					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
  # emb -> embedding
  if name.startswith("""emb."""				):
   _snake_case    :  List[str]					=					name.replace("""emb."""				,       """embeddings."""				)
  # ln_0 -> pre_ln (only present at block 0)
  if name.startswith("""blocks.0.ln0"""				):
   _snake_case    :  List[str]					=					name.replace("""blocks.0.ln0"""				,       """blocks.0.pre_ln"""				)
  # att -> attention
  _snake_case    :  List[str]					=					re.sub(R"""blocks\.(\d+)\.att"""				,       R"""blocks.\1.attention"""				,       SCREAMING_SNAKE_CASE__				)
  # ffn -> feed_forward
  _snake_case    :  int					=					re.sub(R"""blocks\.(\d+)\.ffn"""				,       R"""blocks.\1.feed_forward"""				,       SCREAMING_SNAKE_CASE__				)
  # time_mix_k -> time_mix_key and reshape
  if name.endswith(""".time_mix_k"""				):
   _snake_case    :  Union[str, Any]					=					name.replace(""".time_mix_k"""				,       """.time_mix_key"""				)
  # time_mix_v -> time_mix_value and reshape
  if name.endswith(""".time_mix_v"""				):
   _snake_case    :  str					=					name.replace(""".time_mix_v"""				,       """.time_mix_value"""				)
  # time_mix_r -> time_mix_key and reshape
  if name.endswith(""".time_mix_r"""				):
   _snake_case    :  Optional[int]					=					name.replace(""".time_mix_r"""				,       """.time_mix_receptance"""				)

  if name != "head.weight":
   _snake_case    :  List[str]					=					"""rwkv.""" + name

  _snake_case    :  Optional[Any]					=					weight
 return state_dict



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Any=None				,       SCREAMING_SNAKE_CASE__       :	List[Any]=None				,       SCREAMING_SNAKE_CASE__       :	Any=False				,       SCREAMING_SNAKE_CASE__       :	int=None				)  ->     str:
 # 1. If possible, build the tokenizer.
 if tokenizer_file is None:
  print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
  _snake_case    :  Dict					=					50_277
  _snake_case    :  Optional[int]					=					AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
 else:
  _snake_case    :  Any					=					PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Optional[Any]					=					len(SCREAMING_SNAKE_CASE__				)
 tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__				)

 # 2. Build the config
 _snake_case    :  Optional[int]					=					list(NUM_HIDDEN_LAYERS_MAPPING.keys()				)
 if size is None:
  # Try to infer size from the checkpoint name
  for candidate in possible_sizes:
   if candidate in checkpoint_file:
    _snake_case    :  int					=					candidate
    break
  if size is None:
   raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""				)
 if size not in possible_sizes:
  raise ValueError(F'''`size` should be one of {possible_sizes}, got {size}.'''				)

 _snake_case    :  Union[str, Any]					=					RwkvConfig(
     vocab_size=SCREAMING_SNAKE_CASE__				,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]				,       hidden_size=HIDEN_SIZE_MAPPING[size]				,       )
 config.save_pretrained(SCREAMING_SNAKE_CASE__				)

 # 3. Download model file then convert state_dict
 _snake_case    :  Any					=					hf_hub_download(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Union[str, Any]					=					torch.load(SCREAMING_SNAKE_CASE__				,       map_location="""cpu"""				)
 _snake_case    :  Dict					=					convert_state_dict(SCREAMING_SNAKE_CASE__				)

 # 4. Split in shards and save
 _snake_case    :  int					=					shard_checkpoint(SCREAMING_SNAKE_CASE__				)
 for shard_file, shard in shards.items():
  torch.save(SCREAMING_SNAKE_CASE__				,       os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)				)

 if index is not None:
  _snake_case    :  Optional[int]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  # Save the index as well
  with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as f:
   _snake_case    :  Union[str, Any]					=					json.dumps(SCREAMING_SNAKE_CASE__				,       indent=2				,       sort_keys=SCREAMING_SNAKE_CASE__				) + """\n"""
   f.write(SCREAMING_SNAKE_CASE__				)

  # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
  print(
      """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""				)
  _snake_case    :  Tuple					=					list(shards.keys()				)

  del state_dict
  del shards
  gc.collect()

  for shard_file in shard_files:
   _snake_case    :  List[str]					=					torch.load(os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)				)
   torch.save({k: v.cpu().clone() for k, v in state_dict.items()}				,       os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)				)

 del state_dict
 gc.collect()

 if push_to_hub:
  if model_name is None:
   raise ValueError("""Please provide a `model_name` to push the model to the Hub."""				)
  _snake_case    :  Optional[int]					=					AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__				)
  model.push_to_hub(SCREAMING_SNAKE_CASE__				,       max_shard_size="""2GB"""				)
  tokenizer.push_to_hub(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
  )
  parser.add_argument(
      """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
  )
  parser.add_argument(
      """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
  )
  parser.add_argument(
      """--tokenizer_file""",
      default=None,
      type=str,
      help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
  )
  parser.add_argument(
      """--size""",
      default=None,
      type=str,
      help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
      help="""Push to the Hub the converted model.""",
  )
  parser.add_argument(
      """--model_name""",
      default=None,
      type=str,
      help="""Name of the pushed model on the Hub, including the username / organization.""",
  )

  a__    =	parser.parse_args()
  convert_rmkv_checkpoint_to_hf_format(
      args.repo_id,
      args.checkpoint_file,
      args.output_dir,
      size=args.size,
      tokenizer_file=args.tokenizer_file,
      push_to_hub=args.push_to_hub,
      model_name=args.model_name,
  )








# 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)
