


'''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()

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


def        a__     (					UpperCamelCase_						:      int						):
   UpperCAmelCase__	:str      =      dict((re.sub(r'''@@$''',   '''''',   _UpperCamelCase						), v) if k.endswith('''@@'''						) else (re.sub(r'''$''',   '''</w>''',   _UpperCamelCase						), v) for k, v in d.items()						)
   UpperCAmelCase__	:List[Any]      =      '''<s> <pad> </s> <unk>'''.split()
   # restore the special tokens
   for k in keep_keys:
      del da[F'''{k}</w>''']
      UpperCAmelCase__	:Any      =      d[k]  # restore
   return da


def        a__     (					UpperCamelCase_						:      Optional[int],   UpperCamelCase_						:      Dict						):
   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

   UpperCAmelCase__	:Union[str, Any]      =      basename(_UpperCamelCase						)
   UpperCAmelCase__	:Tuple      =      dirname(_UpperCamelCase						)

   UpperCAmelCase__	:Optional[int]      =      fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
   UpperCAmelCase__	:Any      =      cls.hub_models()
   UpperCAmelCase__	:str      =      {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
   UpperCAmelCase__	:List[str]      =      '''.'''
   # 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}'''						)
   UpperCAmelCase__	:str      =      hub_utils.from_pretrained(
       _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase,   archive_map=_UpperCamelCase,   **_UpperCamelCase						)

   UpperCAmelCase__	:str      =      vars(chkpt['''args''']['''model''']						)

   UpperCAmelCase__	:Any      =      args['''source_lang''']
   UpperCAmelCase__	:Dict      =      args['''target_lang''']

   UpperCAmelCase__	:Any      =      dirname(_UpperCamelCase						)
   UpperCAmelCase__	:str      =      basename(_UpperCamelCase						)

   # dicts
   UpperCAmelCase__	:Union[str, Any]      =      os.path.join(_UpperCamelCase,   F'''dict.{src_lang}.txt'''						)
   UpperCAmelCase__	:Optional[Any]      =      os.path.join(_UpperCamelCase,   F'''dict.{tgt_lang}.txt'''						)

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

   UpperCAmelCase__	:Union[str, Any]      =      Dictionary.load(_UpperCamelCase						)
   UpperCAmelCase__	:str      =      rewrite_dict_keys(tgt_dict.indices						)
   UpperCAmelCase__	:List[str]      =      len(_UpperCamelCase						)
   UpperCAmelCase__	:List[str]      =      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)
   UpperCAmelCase__	:List[str]      =      os.path.join(_UpperCamelCase,   VOCAB_FILES_NAMES['''merges_file''']						)
   for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
      UpperCAmelCase__	:List[Any]      =      os.path.join(_UpperCamelCase,   _UpperCamelCase						)
      if os.path.exists(_UpperCamelCase						):
         break
   with open(_UpperCamelCase,   encoding='''utf-8'''						) as fin:
      UpperCAmelCase__	:Dict      =      fin.read()
   UpperCAmelCase__	:Union[str, Any]      =      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
   UpperCAmelCase__	: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"]}'''

   UpperCAmelCase__	:List[str]      =      {
       '''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
   UpperCAmelCase__	:Optional[int]      =      5
   UpperCAmelCase__	:int      =      False
   if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
      UpperCAmelCase__	:List[str]      =      best_score_hparams[model_dir]['''length_penalty''']
   else:
      UpperCAmelCase__	: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
   UpperCAmelCase__	:Union[str, Any]      =      os.path.join(_UpperCamelCase,   _UpperCamelCase						)

   UpperCAmelCase__	:List[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
   UpperCAmelCase__	:int      =      chkpt['''models'''][0]
   UpperCAmelCase__	:str      =      model.state_dict()

   # rename keys to start with 'model.'
   UpperCAmelCase__	:Union[str, Any]      =      OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()						)

   # remove unneeded keys
   UpperCAmelCase__	: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(_UpperCamelCase,   _UpperCamelCase						)

   UpperCAmelCase__	:Optional[Any]      =      FSMTConfig.from_pretrained(_UpperCamelCase						)
   UpperCAmelCase__	:int      =      FSMTForConditionalGeneration(_UpperCamelCase						)

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

   # save
   UpperCAmelCase__	:List[str]      =      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__":
    __lowerCamelCase												=						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.'''
    )
    __lowerCamelCase												=						parser.parse_args()
    convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_		:     int          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]





def       __a      (  _UpperCamelCase:       List[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          _snake_case				= k.replace(_UpperCamelCase   ,		_UpperCamelCase       )

     if k.startswith("encoder"       ):
          _snake_case				= k.replace(".attn"   ,		".self_attn"       )
          _snake_case				= k.replace("norm1"   ,		"self_attn_layer_norm"       )
          _snake_case				= k.replace("norm2"   ,		"final_layer_norm"       )
     elif k.startswith("decoder"       ):
          _snake_case				= k.replace("norm1"   ,		"self_attn_layer_norm"       )
          _snake_case				= k.replace("norm2"   ,		"encoder_attn_layer_norm"       )
          _snake_case				= k.replace("norm3"   ,		"final_layer_norm"       )
     return k





def       __a      (  _UpperCamelCase:       Dict       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= [
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          _snake_case				= sd.pop(_UpperCamelCase       )
          _snake_case				= k.replace("layernorm_embedding"   ,		"layer_norm"       )
          assert new_k not in sd
          _snake_case				= v


UpperCamelCase_		:     Union[str, Any]          =		['''START''']





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Union[str, Any]       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= torch.load(_UpperCamelCase   ,		map_location="cpu"       )
     _snake_case				= model["model"]
     _snake_case				= BlenderbotConfig.from_json_file(_UpperCamelCase       )
     _snake_case				= BlenderbotForConditionalGeneration(_UpperCamelCase       )
     _snake_case				= m.model.state_dict().keys()
     _snake_case				= []
     _snake_case				= {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          _snake_case				= rename_state_dict_key(_UpperCamelCase       )
          if new_k not in valid_keys:
               failures.append([k, new_k]       )
          else:
               _snake_case				= v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(_UpperCamelCase       )
     m.model.load_state_dict(_UpperCamelCase   ,		strict=_UpperCamelCase       )
     m.half()
     m.save_pretrained(_UpperCamelCase       )


if __name__ == "__main__":
   UpperCamelCase_		:     Any          =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
   parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
   parser.add_argument(
       '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
   )
   UpperCamelCase_		:     Optional[int]          =		parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


