



# 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__      :				str   	=			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__      :				Dict   	=			{
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.1_5},
    "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__      :				List[str]   	=			{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     a__      :				Optional[int]   	=			"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__      :				str   	=			"allenai"

def   _lowerCAmelCase   (						A__	):
      # (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}
      lowercase__     =  dict((re.sub(r'@@$'						,  ''						,  snake_case_	), v) if k.endswith('@@'	) else (re.sub(r'$'						,  '</w>'						,  snake_case_	), v) for k, v in d.items()	)
      lowercase__     =  """<s> <pad> </s> <unk>""".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F'''{k}</w>''']
            lowercase__     =  d[k]  # restore
      return da

def   _lowerCAmelCase   (						A__						,  A__	):
      # prep
      assert os.path.exists(snake_case_	)
      os.makedirs(snake_case_						,  exist_ok=snake_case_	)
      print(F'''Writing results to {pytorch_dump_folder_path}'''	)

      # handle various types of models

      lowercase__     =  basename(snake_case_	)
      lowercase__     =  dirname(snake_case_	)

      lowercase__     =  fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
      lowercase__     =  cls.hub_models()
      lowercase__     =  {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
      lowercase__     =  """."""
      # 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}'''	)
      lowercase__     =  hub_utils.from_pretrained(
          snake_case_						,  snake_case_						,  snake_case_						,  archive_map=snake_case_						,  **snake_case_	)

      lowercase__     =  vars(chkpt['args']['model']	)

      lowercase__     =  args["""source_lang"""]
      lowercase__     =  args["""target_lang"""]

      lowercase__     =  dirname(snake_case_	)
      lowercase__     =  basename(snake_case_	)

      # dicts
      lowercase__     =  os.path.join(snake_case_						,  F'''dict.{src_lang}.txt'''	)
      lowercase__     =  os.path.join(snake_case_						,  F'''dict.{tgt_lang}.txt'''	)

      lowercase__     =  Dictionary.load(snake_case_	)
      lowercase__     =  rewrite_dict_keys(src_dict.indices	)
      lowercase__     =  len(snake_case_	)
      lowercase__     =  os.path.join(snake_case_						,  'vocab-src.json'	)
      print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''	)
      with open(snake_case_						,  'w'						,  encoding='utf-8'	) as f:
            f.write(json.dumps(snake_case_						,  ensure_ascii=snake_case_						,  indent=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
      lowercase__     =  True
      for k in src_vocab.keys():
            if not k.islower():
                  lowercase__     =  False
                  break

      lowercase__     =  Dictionary.load(snake_case_	)
      lowercase__     =  rewrite_dict_keys(tgt_dict.indices	)
      lowercase__     =  len(snake_case_	)
      lowercase__     =  os.path.join(snake_case_						,  'vocab-tgt.json'	)
      print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''	)
      with open(snake_case_						,  'w'						,  encoding='utf-8'	) as f:
            f.write(json.dumps(snake_case_						,  ensure_ascii=snake_case_						,  indent=snake_case_	)	)

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

      # model config
      lowercase__     =  os.path.join(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']}'''

      lowercase__     =  {
          """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
      lowercase__     =  5
      lowercase__     =  False
      if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
            lowercase__     =  best_score_hparams[model_dir]["""length_penalty"""]
      else:
            lowercase__     =  1.0

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

      # tokenizer config
      lowercase__     =  os.path.join(snake_case_						,  snake_case_	)

      lowercase__     =  {
          """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(snake_case_						,  'w'						,  encoding='utf-8'	) as f:
            f.write(json.dumps(snake_case_						,  ensure_ascii=snake_case_						,  indent=snake_case_	)	)

      # model
      lowercase__     =  chkpt["""models"""][0]
      lowercase__     =  model.state_dict()

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

      # remove unneeded keys
      lowercase__     =  [
          """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(snake_case_						,  snake_case_	)

      lowercase__     =  FSMTConfig.from_pretrained(snake_case_	)
      lowercase__     =  FSMTForConditionalGeneration(snake_case_	)

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

      # save
      lowercase__     =  os.path.join(snake_case_						,  snake_case_	)
      print(F'''Generating {pytorch_weights_dump_path}'''	)
      torch.save(snake_case_						,  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__      :				Union[str, Any]   	=			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__      :				Optional[Any]   	=			parser.parse_args()
     convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


a__      :				int   	=			logging.get_logger(__name__)

a__      :				Tuple   	=			{"vocab_file": "vocab.txt"}

a__      :				int   	=			{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

a__      :				Dict   	=			{
    "facebook/esm2_t6_8M_UR50D": 10_24,
    "facebook/esm2_t12_35M_UR50D": 10_24,
}

def   _lowerCAmelCase   (						A__	):
      with open(A__						,  'r'	) as f:
            lowercase__     =  f.read().splitlines()
            return [l.strip() for l in lines]



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    VOCAB_FILES_NAMES
   A    :  str       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  int       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  List[Any]       				=    ["input_ids", "attention_mask"]


   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Optional[int]="<unk>"			,				lowerCAmelCase  :			Dict="<cls>"			,				lowerCAmelCase  :			List[str]="<pad>"			,				lowerCAmelCase  :			Union[str, Any]="<mask>"			,				lowerCAmelCase  :			Optional[Any]="<eos>"			,				**lowerCAmelCase  :			Any			,				)	->				Optional[Any]:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)
         lowercase__     =  load_vocab_file(lowerCAmelCase)
         lowercase__     =  dict(enumerate(self.all_tokens))
         lowercase__     =  {tok: ind for ind, tok in enumerate(self.all_tokens)}
         lowercase__     =  unk_token
         lowercase__     =  cls_token
         lowercase__     =  pad_token
         lowercase__     =  mask_token
         lowercase__     =  eos_token
         lowercase__     =  self.all_tokens
         self._create_trie(self.unique_no_split_tokens)


   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         return self._id_to_token.get(lowerCAmelCase			,				self.unk_token)


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         return self._token_to_id.get(lowerCAmelCase			,				self._token_to_id.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			str			,				**lowerCAmelCase  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         return text.split()


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Any=False)	->				Union[str, Any]:




         """simple docstring"""

         return len(self._id_to_token)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

         return {token: i for i, token in enumerate(self.all_tokens)}


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         return self._token_to_id.get(lowerCAmelCase			,				self._token_to_id.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         return self._id_to_token.get(lowerCAmelCase			,				self.unk_token)


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			List[int]			,				lowerCAmelCase  :			Optional[List[int]] = None)	->				List[int]:




         """simple docstring"""

         lowercase__     =  [self.cls_token_id]
         lowercase__     =  [self.eos_token_id]  # No sep token in ESM vocabulary
         if token_ids_a is None:
               if self.eos_token_id is None:
                     return cls + token_ids_a
               else:
                     return cls + token_ids_a + sep
         elif self.eos_token_id is None:
               raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!')
         return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			List			,				lowerCAmelCase  :			Optional[List] = None			,				lowerCAmelCase  :			bool = False)	->				List[int]:




         """simple docstring"""

         if already_has_special_tokens:
               if token_ids_a is not None:
                     raise ValueError(
                         'You should not supply a second sequence if the provided sequence of '
                         'ids is already formatted with special tokens for the model.')

               return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
         lowercase__     =  [1] + ([0] * len(lowerCAmelCase)) + [1]
         if token_ids_a is not None:
               mask += [0] * len(lowerCAmelCase) + [1]
         return mask


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  os.path.join(lowerCAmelCase			,				(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt')
         with open(lowerCAmelCase			,				'w') as f:
               f.write('\n'.join(self.all_tokens))
         return (vocab_file,)


   @property
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				int:




         """simple docstring"""

         return self.get_vocab_size(with_added_tokens=lowerCAmelCase)



   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			Union[List[str], List[AddedToken]]			,				lowerCAmelCase  :			bool = False)	->				int:




         """simple docstring"""

         return super()._add_tokens(lowerCAmelCase			,				special_tokens=lowerCAmelCase)
