





'''simple docstring'''

# 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     __magic_name__							(				__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							dict((re.sub(R"""@@$"""				,						""""""				,						__UpperCAmelCase   ), v) if k.endswith("""@@"""   ) else (re.sub(R"""$"""				,						"""</w>"""				,						__UpperCAmelCase   ), v) for k, v in d.items()   )
   __SCREAMING_SNAKE_CASE   				=							"""<s> <pad> </s> <unk>""".split()
   # restore the special tokens
   for k in keep_keys:
      del da[f"""{k}</w>"""]
      __SCREAMING_SNAKE_CASE   				=							d[k]  # restore
   return da

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   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

   __SCREAMING_SNAKE_CASE   				=							basename(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							dirname(__UpperCAmelCase   )

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

   __SCREAMING_SNAKE_CASE   				=							vars(chkpt["""args"""]["""model"""]   )

   __SCREAMING_SNAKE_CASE   				=							args["""source_lang"""]
   __SCREAMING_SNAKE_CASE   				=							args["""target_lang"""]

   __SCREAMING_SNAKE_CASE   				=							dirname(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							basename(__UpperCAmelCase   )

   # dicts
   __SCREAMING_SNAKE_CASE   				=							os.path.join(__UpperCAmelCase				,						f"""dict.{src_lang}.txt"""   )
   __SCREAMING_SNAKE_CASE   				=							os.path.join(__UpperCAmelCase				,						f"""dict.{tgt_lang}.txt"""   )

   __SCREAMING_SNAKE_CASE   				=							Dictionary.load(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							rewrite_dict_keys(src_dict.indices   )
   __SCREAMING_SNAKE_CASE   				=							len(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							True
   for k in src_vocab.keys():
      if not k.islower():
         __SCREAMING_SNAKE_CASE   				=							False
         break

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

   __SCREAMING_SNAKE_CASE   				=							{
       """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
   __SCREAMING_SNAKE_CASE   				=							5
   __SCREAMING_SNAKE_CASE   				=							False
   if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
      __SCREAMING_SNAKE_CASE   				=							best_score_hparams[model_dir]["""length_penalty"""]
   else:
      __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							os.path.join(__UpperCAmelCase				,						__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							{
       """langs""": [src_lang, tgt_lang],
       """model_max_length""": 1024,
       """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
   __SCREAMING_SNAKE_CASE   				=							chkpt["""models"""][0]
   __SCREAMING_SNAKE_CASE   				=							model.state_dict()

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

   # remove unneeded keys
   __SCREAMING_SNAKE_CASE   				=							[
       """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   )

   __SCREAMING_SNAKE_CASE   				=							FSMTConfig.from_pretrained(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							FSMTForConditionalGeneration(__UpperCAmelCase   )

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

   # save
   __SCREAMING_SNAKE_CASE   				=							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__":
    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)







"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''▁'''

_lowercase				 =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase				 =					{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowercase				 =					{
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     VOCAB_FILES_NAMES
						_lowerCamelCase:     List[str]   =     PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:     Union[str, Any]   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:     Any   =     ['''input_ids''', '''attention_mask''']


						def __init__(			self  :   Union[str, Any]	,A_  :   str	,A_  :   str="<s>"	,A_  :   Any="</s>"	,A_  :   Tuple="</s>"	,A_  :   Any="<s>"	,A_  :   Optional[Any]="<unk>"	,A_  :   int="<pad>"	,A_  :   str="<mask>"	,A_  :   Optional[Dict[str, Any]] = None	,**A_  :   Optional[int]	,)  -> None:
							# Mask token behave like a normal word, i.e. include the space before it
							A     = AddedToken(A_	,lstrip=A_	,rstrip=A_   ) if isinstance(A_	,A_   ) else mask_token

							A     = {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_	,eos_token=A_	,unk_token=A_	,sep_token=A_	,cls_token=A_	,pad_token=A_	,mask_token=A_	,sp_model_kwargs=self.sp_model_kwargs	,**A_	,)

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A_   )   )
							A     = vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							A     = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							A     = 1

							A     = len(self.sp_model   ) + self.fairseq_offset
							A     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}


						def __getstate__(			self  :   Union[str, Any]   )  -> Any:
							A     = self.__dict__.copy()
							A     = None
							A     = self.sp_model.serialized_model_proto()
							return state


						def __setstate__(			self  :   str	,A_  :   str   )  -> Optional[Any]:
							A     = d

							# for backward compatibility
							if not hasattr(self	,'sp_model_kwargs'   ):
								A     = {}

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:

							if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							A     = [self.cls_token_id]
							A     = [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   List[int]	,A_  :   Optional[List[int]] = None	,A_  :   bool = False   )  -> List[int]:

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_	,token_ids_a=A_	,already_has_special_tokens=A_   )

							if token_ids_a is None:
								return [1] + ([0] * len(A_   )) + [1]
							return [1] + ([0] * len(A_   )) + [1, 1] + ([0] * len(A_   )) + [1]


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:
							A     = [self.sep_token_id]
							A     = [self.cls_token_id]

							if token_ids_a is None:
								return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[Any]:
							return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							A     = {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> List[str]:
							return self.sp_model.encode(A_	,out_type=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[Any]   )  -> Tuple:
							if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
							A     = self.sp_model.PieceToId(A_   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> int:
							if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   Optional[Any]   )  -> List[Any]:
							A     = ''.join(A_   ).replace(A_	,' '   ).strip()
							return out_string




						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   str	,A_  :   Optional[str] = None   )  -> Tuple[str]:
							if not os.path.isdir(A_   ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
								return
							A     = os.path.join(
							    A_	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(A_   ) and os.path.isfile(self.vocab_file   ):
								copyfile(self.vocab_file	,A_   )
							elif not os.path.isfile(self.vocab_file   ):
								with open(A_	,'wb'   ) as fi:
									A     = self.sp_model.serialized_model_proto()
									fi.write(A_   )

							return (out_vocab_file,)