


"""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()

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






def       lowerCamelCase					(a_						:Dict)     ->							str:
       # (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		:str       							=				dict((re.sub(R'''@@$'''				,				''''''				,				a_), v) if k.endswith('''@@''') else (re.sub(R'''$'''				,				'''</w>'''				,				a_), v) for k, v in d.items())
       lowercase		:Dict       							=				'''<s> <pad> </s> <unk>'''.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"""{k}</w>"""]
              lowercase		:str       							=				d[k]  # restore
       return da






def       lowerCamelCase					(a_						:Any				,				a_						:Tuple)     ->							List[str]:
       # prep
       assert os.path.exists(a_)
       os.makedirs(a_				,				exist_ok=a_)
       print(F"""Writing results to {pytorch_dump_folder_path}""")

       # handle various types of models

       lowercase		:List[str]       							=				basename(a_)
       lowercase		:Dict       							=				dirname(a_)

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

       lowercase		:int       							=				vars(chkpt['''args''']['''model'''])

       lowercase		:List[str]       							=				args['''source_lang''']
       lowercase		:Optional[int]       							=				args['''target_lang''']

       lowercase		:Optional[int]       							=				dirname(a_)
       lowercase		:str       							=				basename(a_)

       # dicts
       lowercase		:Dict       							=				os.path.join(a_				,				F"""dict.{src_lang}.txt""")
       lowercase		:List[str]       							=				os.path.join(a_				,				F"""dict.{tgt_lang}.txt""")

       lowercase		:Dict       							=				Dictionary.load(a_)
       lowercase		:Dict       							=				rewrite_dict_keys(src_dict.indices)
       lowercase		:Union[str, Any]       							=				len(a_)
       lowercase		:Any       							=				os.path.join(a_				,				'''vocab-src.json''')
       print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records""")
       with open(a_				,				'''w'''				,				encoding='''utf-8''') as f:
              f.write(json.dumps(a_				,				ensure_ascii=a_				,				indent=a_))

       # 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		:Tuple       							=				True
       for k in src_vocab.keys():
              if not k.islower():
                     lowercase		:Dict       							=				False
                     break

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

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

       # model config
       lowercase		:List[Any]       							=				os.path.join(a_				,				'''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		:List[Any]       							=				{
           '''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		:Union[str, Any]       							=				5
       lowercase		:Tuple       							=				False
       if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
              lowercase		:Union[str, Any]       							=				best_score_hparams[model_dir]['''length_penalty''']
       else:
              lowercase		:Tuple       							=				1.0

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

       # tokenizer config
       lowercase		:Tuple       							=				os.path.join(a_				,				a_)

       lowercase		:str       							=				{
           '''langs''': [src_lang, tgt_lang],
           '''model_max_length''': 1024,
           '''do_lower_case''': do_lower_case,
       }

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

       # model
       lowercase		:Optional[int]       							=				chkpt['''models'''][0]
       lowercase		:int       							=				model.state_dict()

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

       # remove unneeded keys
       lowercase		:Optional[int]       							=				[
           '''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(a_				,				a_)

       lowercase		:Any       							=				FSMTConfig.from_pretrained(a_)
       lowercase		:Dict       							=				FSMTForConditionalGeneration(a_)

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

       # save
       lowercase		:str       							=				os.path.join(a_				,				a_)
       print(F"""Generating {pytorch_weights_dump_path}""")
       torch.save(a_				,				a_)

       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__":
 UpperCAmelCase      		=			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.'''
 )
 UpperCAmelCase      		=			parser.parse_args()
 convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCAmelCase      		=			logging.get_logger(__name__)


UpperCAmelCase      		=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase      		=			{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase      		=			{
    '''facebook/bart-base''': 1_024,
    '''facebook/bart-large''': 1_024,
    '''facebook/bart-large-mnli''': 1_024,
    '''facebook/bart-large-cnn''': 1_024,
    '''facebook/bart-large-xsum''': 1_024,
    '''yjernite/bart_eli5''': 1_024,
}
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      str        =   VOCAB_FILES_NAMES
       __A			:      int        =   PRETRAINED_VOCAB_FILES_MAP
       __A			:      List[Any]        =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __A			:      Optional[int]        =   ["input_ids", "attention_mask"]
       __A			:      List[Any]        =   BartTokenizer






       def __init__(   self  :    List[Any]  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Optional[int]="replace"  ,     snake_case__  :    int="<s>"  ,     snake_case__  :    Optional[Any]="</s>"  ,     snake_case__  :    str="</s>"  ,     snake_case__  :    Union[str, Any]="<s>"  ,     snake_case__  :    Optional[Any]="<unk>"  ,     snake_case__  :    Tuple="<pad>"  ,     snake_case__  :    str="<mask>"  ,     snake_case__  :    List[Any]=False  ,     snake_case__  :    Tuple=True  ,     **snake_case__  :    Dict  ,     ):

              '''simple docstring'''






              super().__init__(
                  snake_case__  ,     snake_case__  ,     tokenizer_file=snake_case__  ,     errors=snake_case__  ,     bos_token=snake_case__  ,     eos_token=snake_case__  ,     sep_token=snake_case__  ,     cls_token=snake_case__  ,     unk_token=snake_case__  ,     pad_token=snake_case__  ,     mask_token=snake_case__  ,     add_prefix_space=snake_case__  ,     trim_offsets=snake_case__  ,     **snake_case__  ,     )

              lowercase		:Dict       							=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
              if pre_tok_state.get('''add_prefix_space'''  ,     snake_case__				) != add_prefix_space:
                     lowercase		:Union[str, Any]       							=				getattr(snake_case__  ,     pre_tok_state.pop('''type'''				)				)
                     lowercase		:Tuple       							=				add_prefix_space
                     lowercase		:int       							=				pre_tok_class(**snake_case__				)

              lowercase		:str       							=				add_prefix_space

              # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
              lowercase		:Dict       							=				'''post_processor'''
              lowercase		:Optional[int]       							=				getattr(self.backend_tokenizer  ,     snake_case__  ,     snake_case__				)
              if tokenizer_component_instance:
                     lowercase		:Tuple       							=				json.loads(tokenizer_component_instance.__getstate__()				)

                     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                     if "sep" in state:
                            lowercase		:Optional[Any]       							=				tuple(state['''sep''']				)
                     if "cls" in state:
                            lowercase		:Tuple       							=				tuple(state['''cls''']				)

                     lowercase		:int       							=				False

                     if state.get('''add_prefix_space'''  ,     snake_case__				) != add_prefix_space:
                            lowercase		:Dict       							=				add_prefix_space
                            lowercase		:Optional[int]       							=				True

                     if state.get('''trim_offsets'''  ,     snake_case__				) != trim_offsets:
                            lowercase		:Any       							=				trim_offsets
                            lowercase		:Dict       							=				True

                     if changes_to_apply:
                            lowercase		:Dict       							=				getattr(snake_case__  ,     state.pop('''type'''				)				)
                            lowercase		:List[str]       							=				component_class(**snake_case__				)
                            setattr(self.backend_tokenizer  ,     snake_case__  ,     snake_case__				)






       @property
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              if self._mask_token is None:
                     if self.verbose:
                            logger.error('''Using mask_token, but it is not set yet.'''				)
                     return None
              return str(self._mask_token				)






       @mask_token.setter
       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Dict       							=				AddedToken(snake_case__  ,     lstrip=snake_case__  ,     rstrip=snake_case__				) if isinstance(snake_case__  ,     snake_case__				) else value
              lowercase		:Dict       							=				value






       def  __snake_case						(   self  :    Optional[int]  ,     *snake_case__  :    str  ,     **snake_case__  :    str				):

              '''simple docstring'''






              lowercase		:Any       							=				kwargs.get('''is_split_into_words'''  ,     snake_case__				)

              if is_split_into_words and not self.add_prefix_space:
                     raise ValueError(
                         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                         '''to use it with pretokenized inputs.'''				)

              return super()._batch_encode_plus(*snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    int  ,     *snake_case__  :    int  ,     **snake_case__  :    int				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				kwargs.get('''is_split_into_words'''  ,     snake_case__				)

              if is_split_into_words and not self.add_prefix_space:
                     raise ValueError(
                         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                         '''to use it with pretokenized inputs.'''				)

              return super()._encode_plus(*snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    str  ,     snake_case__  :    Optional[str] = None				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				self._tokenizer.model.save(snake_case__  ,     name=snake_case__				)
              return tuple(snake_case__				)






       def  __snake_case						(   self  :    int  ,     snake_case__  :    Any  ,     snake_case__  :    int=None				):

              '''simple docstring'''






              lowercase		:Any       							=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
              if token_ids_a is None:
                     return output

              return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    List[int]  ,     snake_case__  :    Optional[List[int]] = None				):

              '''simple docstring'''






              lowercase		:int       							=				[self.sep_token_id]
              lowercase		:Tuple       							=				[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]

