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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    _SCREAMING_SNAKE_CASE										=						None


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE										=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE										=						{
    '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'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE										=						{
    'facebook/mbart-large-en-ro': 1_024,
    'facebook/mbart-large-cc25': 1_024,
}

# fmt: off
_SCREAMING_SNAKE_CASE										=						['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							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:List[Any]    			=		VOCAB_FILES_NAMES
 lowerCamelCase	:Tuple    			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase	:Optional[Any]    			=		PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase	:Dict    			=		['''input_ids''', '''attention_mask''']
 lowerCamelCase	:Optional[Any]    			=		MBartTokenizer

 lowerCamelCase	:List[int]    			=		[]
 lowerCamelCase	:List[int]    			=		[]



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

        super().__init__(
            vocab_file=lowerCAmelCase_		,				tokenizer_file=lowerCAmelCase_		,				bos_token=lowerCAmelCase_		,				eos_token=lowerCAmelCase_		,				sep_token=lowerCAmelCase_		,				cls_token=lowerCAmelCase_		,				unk_token=lowerCAmelCase_		,				pad_token=lowerCAmelCase_		,				mask_token=lowerCAmelCase_		,				src_lang=lowerCAmelCase_		,				tgt_lang=lowerCAmelCase_		,				additional_special_tokens=lowerCAmelCase_		,				**lowerCAmelCase_		,				)

        _A        =  vocab_file
        _A        =  False if not self.vocab_file else True

        _A        =  FAIRSEQ_LANGUAGE_CODES.copy()

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

        self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}							)
        _A        =  {
            lang_code: self.convert_tokens_to_ids(lowerCAmelCase_							) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        _A        =  src_lang if src_lang is not None else """en_XX"""
        _A        =  self.convert_tokens_to_ids(self._src_lang							)
        _A        =  tgt_lang
        self.set_src_lang_special_tokens(self._src_lang							)



 @property
 def      UpperCAmelCase							(					self							)  ->					str:
        return self._src_lang



 @src_lang.setter
 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					None:
        _A        =  new_src_lang
        self.set_src_lang_special_tokens(self._src_lang							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					List[int]:
        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      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = 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]



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					Any:
        if src_lang is None or tgt_lang is None:
               raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""							)
        _A        =  src_lang
        _A        =  self(lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_		,				return_tensors=lowerCAmelCase_		,				**lowerCAmelCase_							)
        _A        =  self.convert_tokens_to_ids(lowerCAmelCase_							)
        _A        =  tgt_lang_id
        return inputs



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = "en_XX"		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = "ro_RO"		,				**lowerCAmelCase_		,				)  ->					BatchEncoding:
        _A        =  src_lang
        _A        =  tgt_lang
        return super().prepare_seqaseq_batch(lowerCAmelCase_		,				lowerCAmelCase_		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        return self.set_src_lang_special_tokens(self.src_lang							)



 def      UpperCAmelCase							(					self							)  ->					str:
        return self.set_tgt_lang_special_tokens(self.tgt_lang							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					None:
        _A        =  self.convert_tokens_to_ids(lowerCAmelCase_							)
        _A        =  []
        _A        =  [self.eos_token_id, self.cur_lang_code]

        _A        =  self.convert_ids_to_tokens(self.prefix_tokens							)
        _A        =  self.convert_ids_to_tokens(self.suffix_tokens							)

        _A        =  processors.TemplateProcessing(
            single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str		,				pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str		,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,				self.prefix_tokens + self.suffix_tokens							)							)		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					None:
        _A        =  self.convert_tokens_to_ids(lowerCAmelCase_							)
        _A        =  []
        _A        =  [self.eos_token_id, self.cur_lang_code]

        _A        =  self.convert_ids_to_tokens(self.prefix_tokens							)
        _A        =  self.convert_ids_to_tokens(self.suffix_tokens							)

        _A        =  processors.TemplateProcessing(
            single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str		,				pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str		,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,				self.prefix_tokens + self.suffix_tokens							)							)		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					Tuple[str]:
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                   """tokenizer."""							)

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

        if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCAmelCase_							):
               copyfile(self.vocab_file		,				lowerCAmelCase_							)

        return (out_vocab_file,)





import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def  snake_case  (       snake_case__			:int   ,	snake_case__			:List[str]   ,	snake_case__			:Union[str, Any])						->   str:
       # Initialise PyTorch model
       _A        =  AlbertConfig.from_json_file(snake_case__)
       print(F'''Building PyTorch model from configuration: {config}''')
       _A        =  AlbertForPreTraining(snake_case__)

       # Load weights from tf checkpoint
       load_tf_weights_in_albert(snake_case__   ,	snake_case__   ,	snake_case__)

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


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						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(
        '--albert_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained ALBERT model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    _SCREAMING_SNAKE_CASE										=						parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)




