
"""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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


a					:	List[Any]							=		logging.get_logger(__name__)

a					:	Tuple							=		'''▁'''

a					:	int							=		{'''vocab_file''': '''sentencepiece.bpe.model'''}

a					:	Optional[int]							=		{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

a					:	List[str]							=		{
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
a					:	List[Any]							=		['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']




class 		a_						( _UpperCAmelCase	):
    a      :				Tuple     =				VOCAB_FILES_NAMES
    a      :				Optional[int]     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a      :				Union[str, Any]     =				PRETRAINED_VOCAB_FILES_MAP
    a      :				Optional[Any]     =				['input_ids', 'attention_mask']

    a      :				List[int]     =				[]
    a      :				List[int]     =				[]







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






        '''simple docstring'''






        _UpperCAmelCase          =   AddedToken(__UpperCamelCase     ,					lstrip=__UpperCamelCase     ,					rstrip=__UpperCamelCase     ) if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) else mask_token

        _UpperCAmelCase          =   {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__UpperCamelCase     ,					eos_token=__UpperCamelCase     ,					unk_token=__UpperCamelCase     ,					sep_token=__UpperCamelCase     ,					cls_token=__UpperCamelCase     ,					pad_token=__UpperCamelCase     ,					mask_token=__UpperCamelCase     ,					tokenizer_file=__UpperCamelCase     ,					src_lang=__UpperCamelCase     ,					tgt_lang=__UpperCamelCase     ,					additional_special_tokens=__UpperCamelCase     ,					sp_model_kwargs=self.sp_model_kwargs     ,					**__UpperCamelCase     ,					)

        _UpperCAmelCase          =   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
        self.sp_model.Load(str(__UpperCamelCase     )     )
        _UpperCAmelCase          =   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
        _UpperCAmelCase          =   {"""<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
        _UpperCAmelCase          =   1

        _UpperCAmelCase          =   len(self.sp_model     )
        _UpperCAmelCase          =   {
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__UpperCamelCase     )
        }
        _UpperCAmelCase          =   {v: k for k, v in self.lang_code_to_id.items()}
        _UpperCAmelCase          =   len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id     )
        _UpperCAmelCase          =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        _UpperCAmelCase          =   list(self.lang_code_to_id.keys()     )

        if additional_special_tokens is not None:
            # Only add those special tokens if they are not already there.
            self._additional_special_tokens.extend(
                [t for t in additional_special_tokens if t not in self._additional_special_tokens]     )

        _UpperCAmelCase          =   src_lang if src_lang is not None else """en_XX"""
        _UpperCAmelCase          =   self.lang_code_to_id[self._src_lang]
        _UpperCAmelCase          =   tgt_lang
        self.set_src_lang_special_tokens(self._src_lang     )







    def __getstate__(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.__dict__.copy()
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   self.sp_model.serialized_model_proto()
        return state







    def __setstate__(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Dict     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   d

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

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







    @property
    def      _snake_case					(			self			:       Any     )		->List[str]:






        '''simple docstring'''






        return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token







    @property
    def      _snake_case					(			self			:       Optional[Any]     )		->str:






        '''simple docstring'''






        return self._src_lang







    @src_lang.setter
    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       str     )		->None:






        '''simple docstring'''






        _UpperCAmelCase          =   new_src_lang
        self.set_src_lang_special_tokens(self._src_lang     )







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       List[int]     ,					__UpperCamelCase			:       Optional[List[int]] = None     ,					__UpperCamelCase			:       bool = False     )		->List[int]:






        '''simple docstring'''






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

        _UpperCAmelCase          =   [1] * len(self.prefix_tokens     )
        _UpperCAmelCase          =   [1] * len(self.suffix_tokens     )
        if token_ids_a is None:
            return prefix_ones + ([0] * len(__UpperCamelCase     )) + suffix_ones
        return prefix_ones + ([0] * len(__UpperCamelCase     )) + ([0] * len(__UpperCamelCase     )) + suffix_ones







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       List[int]     ,					__UpperCamelCase			:       Optional[List[int]] = None     )		->List[int]:






        '''simple docstring'''






        if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







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






        '''simple docstring'''






        _UpperCAmelCase          =   [self.sep_token_id]
        _UpperCAmelCase          =   [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]







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Optional[str]     ,					__UpperCamelCase			:       Optional[str]     ,					**__UpperCamelCase			:       Tuple     )		->int:






        '''simple docstring'''






        if src_lang is None or tgt_lang is None:
            raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""     )
        _UpperCAmelCase          =   src_lang
        _UpperCAmelCase          =   self(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     ,					return_tensors=__UpperCamelCase     ,					**__UpperCamelCase     )
        _UpperCAmelCase          =   self.convert_tokens_to_ids(__UpperCamelCase     )
        _UpperCAmelCase          =   tgt_lang_id
        return inputs







    def      _snake_case					(			self			:       Optional[int]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   {self.convert_ids_to_tokens(__UpperCamelCase     ): i for i in range(self.vocab_size     )}
        vocab.update(self.added_tokens_encoder     )
        return vocab







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       str     )		->List[str]:






        '''simple docstring'''






        return self.sp_model.encode(__UpperCamelCase     ,					out_type=__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Any     )		->Union[str, Any]:






        '''simple docstring'''






        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        _UpperCAmelCase          =   self.sp_model.PieceToId(__UpperCamelCase     )

        # 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      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       List[Any]     )		->Tuple:






        '''simple docstring'''






        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      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Dict     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   """""".join(__UpperCamelCase     ).replace(__UpperCamelCase     ,					""" """     ).strip()
        return out_string







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Optional[str] = None     )		->Tuple[str]:






        '''simple docstring'''






        if not os.path.isdir(__UpperCamelCase     ):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
            return
        _UpperCAmelCase          =   os.path.join(
            __UpperCamelCase     ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

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

        return (out_vocab_file,)







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       str = "en_XX"     ,					__UpperCamelCase			:       Optional[List[str]] = None     ,					__UpperCamelCase			:       str = "ro_RO"     ,					**__UpperCamelCase			:       Tuple     ,					)		->BatchEncoding:






        '''simple docstring'''






        _UpperCAmelCase          =   src_lang
        _UpperCAmelCase          =   tgt_lang
        return super().prepare_seqaseq_batch(__UpperCamelCase     ,					__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     )		->Any:






        '''simple docstring'''






        return self.set_src_lang_special_tokens(self.src_lang     )







    def      _snake_case					(			self			:       Optional[int]     )		->Union[str, Any]:






        '''simple docstring'''






        return self.set_tgt_lang_special_tokens(self.tgt_lang     )







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Tuple     )		->None:






        '''simple docstring'''






        _UpperCAmelCase          =   self.lang_code_to_id[src_lang]
        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   [self.eos_token_id, self.cur_lang_code]







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       str     )		->None:






        '''simple docstring'''






        _UpperCAmelCase          =   self.lang_code_to_id[lang]
        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   [self.eos_token_id, self.cur_lang_code]

"""simple docstring"""



import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   LxmertForPreTraining(_A      )

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_A					,      _A					,      _A      )

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
    torch.save(model.state_dict()					,      _A      )


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)